An integrated artificial intelligence and nano-bio-stimulant seed coating system enhances climate resilience through predictive phenotyping

An integrated artificial intelligence and nano-bio-stimulant seed coating system enhances climate resilience through predictive phenotyping | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article An integrated artificial intelligence and nano-bio-stimulant seed coating system enhances climate resilience through predictive phenotyping Majid Ghanbari, Mahdi Nasrabadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9266171/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change intensifies abiotic stresses such as drought and salinity during seed germination, threatening global food security. While nano-bio-stimulant coatings and artificial intelligence for vigor diagnosis have emerged as promising tools, their integration into a smart, closed-loop system remains unexplored. Here, we present a seed enhancement platform combining AI-based predictive phenotyping with nano-bio-stimulant technology. A hybrid Vision Transformer-Deep learning model trained on hyperspectral images (400–1000 nm) of 16,000 seeds achieved an AUC of 0.993 for non-destructive vigor prediction. High-vigor seeds were coated with a multi-layer formulation: a synthetic microbial community (SynCom) of Pseudomonas fluorescens and Bacillus subtilis, overlaid with chitosan nanoparticles infused with L-amino acids and ascorbic acid. Under drought stress, the AI-Selected + Coated group achieved 95% germination and 58% increase in seedling biomass (P = 0.0003), significantly outperforming controls. Biochemical assays confirmed enhanced antioxidant enzyme activity and osmolyte accumulation, indicating priming of stress-responsive pathways. This study demonstrates that merging digital intelligence and nano-biotechnology creates a synergistic, scalable solution for climate-resilient agriculture. Biological sciences/Biotechnology Biological sciences/Plant sciences Digital Phenotyping Microbial SynCom Defense Priming Precision Coating Resource Use Efficiency Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Seed germination and early seedling establishment represent critical bottlenecks in crop production under intensifying abiotic stresses. Drought and salinity- projected to affect over 50% of arable land by 2050- disrupt water uptake, impair metabolic reactivation, and increase oxidative damage during these vulnerable phases, directly compromising stand uniformity and yield potential (Raza et al., 2019 ). Consequently, enhancing seed resilience has emerged as a frontline strategy for climate-adaptive agriculture. Traditional seed enhancement approaches, such as film coating or hydro-priming, primarily offer passive physical protection or short-term hydration benefits but lack mechanisms to actively modulate seed physiology under stress (Montalvo et al., 2016 ). Recent advances in nano-bio-technology have sought to overcome this limitation through engineered seed coatings that deliver bioactive compounds in a controlled manner. For instance, chitosan nanoparticles (CsNPs) can encapsulate osmo-protectants or signaling molecules and release them in response to micro-environmental cues, thereby priming antioxidant and osmo-regulatory pathways (do Espirito Santo Pereira et al., 2021 ; Shang et al., 2019 ). Similarly, plant growth-promoting rhizo-bacteria (PGPR) incorporated into coatings can establish early microbial partnerships that enhance stress tolerance through phyto-hormone modulation or exopolysaccharide production (Olanrewaju et al., 2017 ). While promising, these biological interventions exhibit high variability in field performance- a phenomenon often attributed to unaccounted heterogeneity in seed physiological quality within commercial lots. Parallel developments in digital agriculture offer a complementary solution. Machine learning models, particularly deep convolutional neural networks (CNNs), have demonstrated the capacity to predict seed vigor non-destructively using high-resolution imaging or spectral data (El Sakka et al., 2025 ; Sadeghi-Tehran et al., 2019). Unlike conventional germination tests, which are destructive, time-consuming, and poorly predictive of field emergence, AI-driven phenotyping enables rapid, objective, and scalable assessment of physiological potential. However, to date, these diagnostic tools have remained disconnected from downstream enhancement strategies. Coatings are typically applied uniformly across entire seed lots, regardless of individual seed quality, leading to suboptimal resource use and inconsistent outcomes. This gap highlights a key research need: lacking a unified system that links predictive seed testing with targeted biological upgrades. We hypothesize that the efficacy of nano-bio-stimulant coatings is contingent upon the intrinsic vigor of the seed, and that maximal resilience can only be achieved when high-potential seeds are selectively enhanced. To test this, we developed a closed-loop system that (i) employs a hybrid Vision Transformer–ResNet152 model to predict seed vigor from hyperspectral reflectance, and (ii) applies a multi-layer nano-bio-stimulant coating- comprising a synthetic microbial community (SynCom) and CsNPs loaded with L-amino acids and ascorbic acid- exclusively to AI-identified high-vigor seeds. Here, we evaluate the synergistic effects of this integrated approach on wheat germination, seedling growth, and physiological stress responses under controlled drought conditions. By bridging digital phenotyping and targeted bio-stimulation, this study advances a customized paradigm for seed enhancement that aligns biological inputs with physiological potential, offering a scalable pathway toward climate-resilient crop establishment. 2. Technological Foundations: Architecting a Symbiotic Loop for Seed Resilience The proposed intelligent seed system goes beyond traditional methods by creating a close relationship between biological improvement and digital intelligence. This system is carefully designed to fill the crucial gap between pre-sowing diagnostics and post-sowing performance, a long-standing issue in seed enhancement. We will break down the key technological components-Nano-bio-stimulant engineering and AI-driven phenotyping-that support this integrated approach, explaining their scientific basis and how they combine to enable a new generation of climate-resilient crops. 2.1. Nano-Bio-stimulant Coatings: Precision Engineering of the Spermosphere Environment In this part, we're moving away from traditional coating methods and toward a precise design of a multi-functional nano-based interface, building on our lab's prior experiences. The main idea is that the seed's immediate environment, known as the spermosphere, can be actively shaped to stimulate physiological responses and enhance resilience against environmental stress. This is achieved through a layered coating system, with each layer serving a specific yet complementary purpose. The foundational layer contains a bio-active matrix embedded with a carefully calibrated mix of plant growth-promoting rhizobacteria (PGPR), including Pseudomonas fluorescens SRB-1 and Bacillus subtilis PWN-12. These strains were chosen for their ability to work together. P. fluorescens is known for producing exopolysaccharides that create a protective biofilm, reducing osmotic stress, while B. subtilis produces lipopeptides that help boost the seed's antioxidant and osmoprotectant systems (Olanrewaju et al., 2017 ). This is not just inoculation; it sets up a supportive microbiome. On top of this microbial layer, we have a Nano-carrier system for targeted molecular activation. Chitosan mixed with pyruvate and formed into particles smaller than 100 nm through ionic gelation, serves two roles. Its positive charge ensures it sticks to both the seed coat and microbial surfaces, while its gradual breakdown in the spermosphere allows for a steady release of chito-oligomers. These molecules activate plant defense mechanisms and improve stomatal regulation (Malerba & Cerana, 2016 ; do Espirito Santo Pereira et al., 2021 ). This layered, timed-release method ensures that both biological and molecular actions work together, turning the spermosphere from a passive space into an active area that promotes resilience. 2.2. AI-Driven Vigor Diagnostics: Decoding Phenotypic Potential with Deep Learning The next part tackles a common issue: the inefficiency of applying coatings to varied seed lots, which we've encountered challenges with in our earlier work. Applying complex coatings to genetically diverse seed lots reduces effectiveness and return on investment. Our solution is a non-destructive, predictive screening layer based on deep learning. We move past traditional image processing methods, which rely on manually selected features, to a deep convolutional neural network (CNN) that can learn phenotypic traits directly from raw images. The model is a hybrid Vision Transformer (ViT)-ResNet152 architecture. This model is based on a ResNet152 backbone, pre-trained using ImageNet and further fine-tuned with a specially curated dataset of over 12,000 seed images (Nikon D850, standardized cross-polarized lighting). This setup was chosen for its efficiency and strong gradient flow, essential for working with a smaller dataset. Each image was associated with a verified vigor label from a multi-parameter assessment (germination rate, seedling biomass, ROS scavenging enzyme activity), providing a robust guide for learning. The network identifies subtle phenotypic clues-textural irregularities suggesting cellular damage, spectral differences indicating biochemical conditions, and morphological inconsistencies- linked to physiological potential. This process turns subjective visual evaluation into an objective, numerical, and high-throughput Predictive Vigor Index (PVI). The AI acts as a gatekeeper, ensuring that the Nano-enhancement system only uses seeds with a high capacity to effectively translate biological and molecular signals into healthy seedlings. This is predictive agriculture on a detailed level. 2.3. System Integration: Towards a Closed-Loop Intelligent Agriculture Platform What sets our method apart isn't just developing these technologies separately, but weaving them together deliberately- an idea that emerged from our team discussions at the university. The AI diagnostics provide the initial recommendations, selecting the best seeds. The Nano-coating serves as a targeted solution, designed to strengthen the pre-selected seeds. This creates a beneficial feedback loop: screening enhances the effectiveness of the coating, while the coating ensures that the potential identified by the AI is fully utilized under stress. This system represents a new approach-targeted seed improvement. It tackles the challenge of variability in agricultural products by ensuring the right treatment is applied to the right seed at the right moment. By linking digital phenotyping with physical enhancement, we move from a generic approach to a focused strategy that maximizes resilience, consistency, and yield potential from the crucial early hours of a plant's life. A visual overview of this integrated closed-loop system is presented in (Fig. 1 ). 3. Materials and Methods Use of Artificial Intelligence in Research Large Language Models (LLMs), such as ChatGPT, were not used in the preparation, writing, or editing of this manuscript. Artificial intelligence and machine learning algorithms, specifically a hybrid Vision Transformer-ResNet152 deep learning model, were employed solely for data analysis, image processing, and predictive phenotyping as detailed below. These computational tools were implemented under direct researcher supervision for the specific purposes of seed vigor prediction and statistical modeling, and do not satisfy the criteria for authorship as defined by Scientific Reports. No generative AI tools were utilized for text generation, literature review, or interpretation of results. Seed Material and Experimental Design Seeds of Triticum aestivum L. cv. Pishtaz were obtained from Seed & Plant Improvement Institute, Karaj, Iran. Seeds were stored at 4°C and 15% relative humidity prior to experiments. The experimental design followed a factorial arrangement with two main factors: AI-based selection (selected vs. random) and nano-bio-stimulant coating (coated vs. uncoated), resulting in four treatment groups: (1) AI-Selected + Coated, (2) AI-Selected + Uncoated, (3) Random + Coated, and (4) Random + Uncoated (control). All experiments were conducted in controlled phytotron conditions with three independent biological replicates (n = 3 batches, 100 seeds per treatment per replicate). Nano-bio-stimulant Coating Formulation Chitosan Nanoparticle Synthesis Chitosan nanoparticles (CsNPs) were synthesized via ionic gelation using sodium tripolyphosphate (TPP) as the cross-linking agent. Briefly, chitosan (medium molecular weight, 75–85% deacetylated, Sigma-Aldrich) was dissolved in 1% (v/v) acetic acid at a concentration of 2 mg/mL. Pyruvate-modified chitosan was prepared by adding sodium pyruvate (0.5% w/v) and stirring for 2 h at room temperature. The chitosan solution was then added dropwise to TPP solution (1 mg/mL) under constant magnetic stirring (800 rpm) at a chitosan:TPP ratio of 3:1. The resulting nanoparticle suspension was centrifuged at 12,000 × g for 20 min, washed three times with deionized water, and resuspended in sterile water. Particle size and polydispersity index (PDI) were determined by dynamic light scattering (DLS, Zetasizer Nano ZS, Malvern Instruments). Zeta potential was measured using laser Doppler electrophoresis. Confocal microscopy (Zeiss LSM 800) with fluorescein isothiocyanate (FITC) labeling confirmed nanoparticle morphology and seed coat adhesion. Optimized CsNPs had a mean diameter of 80.4 ± 3.7 nm (PDI = 0.21) and zeta potential of + 38.5 ± 2.1 mV. Bioactive Compound Encapsulation CsNPs were loaded with L-amino acids (L-alanine and L-glutamic acid, 1:1 molar ratio) and ascorbic acid (0.5 mg/mL each) by incubating the nanoparticle suspension with the active compounds for 4 h at 4°C under gentle agitation. Encapsulation efficiency (81.2% ± 2.3) was quantified by high-performance liquid chromatography (HPLC-UV, Agilent 1260 Infinity) following centrifugation and measurement of unencapsulated compounds in the supernatant. Release kinetics were monitored over 96 h using dialysis bags (12–14 kDa molecular weight cutoff) in simulated spermosphere conditions (pH 6.5, 25°C). Synthetic Microbial Community (SynCom) Preparation The synthetic microbial community comprised Pseudomonas fluorescens strain SRB-1 (produces alginate exopolysaccharide) and Bacillus subtilis strain PWN-12 (produces surfactin lipopeptides). Strains were obtained from the [Culture Collection, Institution] and maintained on King's B medium and Luria-Bertani (LB) medium, respectively. For co-culture establishment, single colonies were inoculated into a proprietary minimal medium containing (g/L): glucose 5.0, NH₄NO₃ 1.0, KH₂PO₄ 0.5, Na₂HPO₄ 1.5, MgSO₄·7H₂O 0.2, CaCl₂ 0.01, FeSO₄·7H₂O 0.005, pH 7.0. Cultures were incubated at 28°C with shaking at 180 rpm for 48 h. The SynCom was prepared by mixing equal volumes (1:1) of stationary-phase cultures (OD₆₀₀ = 1.0), resulting in a final titre of 2.5 × 10⁸ colony-forming units (CFU) mL⁻¹. Co-culture stability was confirmed by plate counting on selective media and metabolomic profiling (GC-MS, Agilent 7890B/5977A) for proline, glycine betaine, and surfactin production. Hierarchical Coating Application Seeds were surface-sterilized with 2% (v/v) sodium hypochlorite for 5 min, rinsed thoroughly with sterile water, and air-dried. The coating process involved three sequential steps: Adhesive priming : Seeds were immersed in 1% (w/v) pectin solution for 30 s and flash-dried at 30°C for 2 min. Microbial inoculation : The SynCom suspension was applied using an ultrasonic atomizing nozzle (frequency 120 kHz) to ensure uniform microbial distribution (target density: 10⁶ CFU per seed). Nanoparticle encapsulation : CsNP hydrogel was overlaid to create a moisture-retentive nanofilm, followed by drying at 28°C for 30 min. The precise coating weight gain was maintained at 3.72% (w/w), established through preliminary rheological studies to balance functionality without impeding oxygen diffusion. Coated seeds were stored at 4°C and used within 48 h. AI-Driven Vigor Diagnostics Hyperspectral Imaging System Seed phenotyping was performed using a push-broom hyperspectral imaging system (Specim FX10, Finland) covering the visible to near-infrared range (400–1000 nm) with 5 nm spectral resolution. The system comprised a CMOS sensor (1024 spatial pixels), an imaging spectrograph, and a motorized translation stage. Illumination was provided by two 150 W quartz-tungsten-halogen line lights positioned at 45° angles to minimize specular reflection. Image acquisition was conducted in a darkroom with controlled temperature (22 ± 1°C). Dataset Construction and Preprocessing A total of 16,000 seeds were imaged, with each seed's fate tracked throughout its lifecycle. Raw hyperspectral cubes were corrected for dark current and white reference using SpectralCube software. Regions of interest (ROIs) were segmented using thresholding on the 800 nm band. Reflectance spectra were extracted from each seed and preprocessed using Savitzky-Golay smoothing (window size 11, polynomial order 3) and standard normal variate (SNV) correction. Ground truth labels for model training were established through multi-parameter assessment: germination rate (7-day standard test), seedling biomass (dry weight at 14 days), and reactive oxygen species (ROS) scavenging enzyme activity (superoxide dismutase, catalase, ascorbate peroxidase). Seeds were classified as high-vigor or low-vigor based on composite scoring (top 30% vs. bottom 30% of the distribution). Deep Learning Model Architecture A hybrid Vision Transformer-ResNet152 (ViT-ResNet) architecture was implemented using PyTorch (version 1.12). The model combined: ResNet152 backbone : Pre-trained on ImageNet for local feature extraction from hyperspectral-derived false-color images. Vision Transformer (ViT) : Patch size 16 × 16, embedding dimension 768, 12 transformer layers, 12 attention heads, for capturing global spatial relationships. The hybrid architecture processed 224 × 224 pixel input images. The ResNet backbone extracted convolutional features, which were then flattened and projected into the ViT embedding space. Positional embeddings were added, and the sequence was processed through transformer layers. A classification head (fully connected layer with softmax activation) output the probability of high-vigor classification. Model Training and Validation The dataset was split into training (70%), validation (15%), and test (15%) sets with stratified sampling. Data augmentation included random rotation (± 15°), horizontal flipping, and Gaussian noise (σ = 0.01). Training employed cross-entropy loss with AdamW optimizer (learning rate 1 × 10⁻⁴, weight decay 0.01), batch size 32, and 100 epochs with early stopping (patience = 10). Learning rate scheduling used cosine annealing. Model performance was evaluated using area under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, and F1-score. The final model achieved AUC-ROC = 0.993 (95% CI: 0.989–0.997) on the held-out test set. Explainable AI (XAI) Analysis Model interpretability was assessed using SHAP (SHapley Additive exPlanations) and Layer-wise Relevance Propagation (LRP). SHAP values were computed using the DeepExplainer implementation to identify spectral regions contributing most to classification decisions. LRP was applied to visualize pixel-wise relevance maps. The 750–780 nm near-infrared region was identified as the most predictive spectral window, correlating with seed water content and starch conformation. Abiotic Stress Assays Drought Stress Protocol Controlled drought stress was imposed using polyethylene glycol 6000 (PEG-6000) solutions of increasing osmotic potential in computer-controlled phytotrons (Conviron, Canada). The stress protocol simulated field-relevant conditions: osmotic potential was progressively decreased from − 0.3 MPa (day 1) to − 1.2 MPa (day 10) by daily PEG concentration adjustments. Temperature was maintained at 25/18°C (day/night) with 16 h photoperiod (PAR 400 µmol m⁻² s⁻¹) and 60% relative humidity. Seeds were germinated in sterile quartz sand (particle size 0.5–1.0 mm) in 500 mL pots. Soil water potential was monitored daily using a WP4C dew point potentiometer (Decagon Devices). Canopy hyperspectral imaging (400–1000 nm) and root scanning (WinRHIZO Pro, Regent Instruments) were performed daily for continuous phenotyping. Phenotypic Measurements Germination was recorded daily for 10 days (radicle protrusion ≥ 2 mm). Seedling biomass was determined by drying shoots and roots at 70°C for 48 h to constant weight. Leaf relative water content (RWC) was calculated as (fresh weight - dry weight)/(turgid weight - dry weight) × 100. Chlorophyll fluorescence (Fv/Fm) was measured using a portable fluorometer (PAM-2500, Walz). Statistical Analysis Statistical analyses were performed using R (v4.1.0) and SAS (v9.4). Normality of residuals was assessed using the Shapiro-Wilk test, and homogeneity of variances was tested using Levene's test. Data were log-transformed when necessary to meet assumptions of parametric tests. Response Surface Methodology (RSM) was employed to quantify the interaction between AI selection (categorical factor: selected vs. random) and nano-bio coating (continuous factor: coating weight percentage). A quadratic model was fitted using mixed-effects modeling with seed batch as a random effect to account for inherent variability between biological replicates. The model included main effects, two-way interaction, and quadratic terms for the continuous factor. Model significance was assessed using analysis of variance (ANOVA) with Type III sums of squares. The significance of the AI × Coating interaction term was evaluated using F-tests with Satterthwaite's approximation for degrees of freedom. All tests were two-tailed with α = 0.05. Exact P-values are reported rather than threshold indicators (e.g., "P = 0.003" rather than "P < 0.05"). Data are presented as mean ± standard deviation (s.d.) unless otherwise stated. Sample sizes (n) for each analysis are provided in the figure legends. Multiple comparisons were addressed using Tukey's honestly significant difference (HSD) test for post-hoc analysis following significant ANOVA results. For non-normal data, the Kruskal-Wallis test followed by Dunn's test with Bonferroni correction was applied. 4. Results 4.1. Predictive Phenotyping: AI-Based Vigor Assessment The hybrid Vision Transformer-ResNet152 model achieved exceptional performance in predicting seed vigor from hyperspectral reflectance data. The model reached an AUC-ROC of 0.993 (95% CI: 0.989–0.997) on the held-out test set, successfully categorizing seeds as high- or low-vigor with 98.1% accuracy (Fig. 2 a). Precision, recall, and F1-score for high-vigor classification were 0.97, 0.96, and 0.965, respectively (Table 1 ). Table 1 Performance metrics of the AI vigor prediction model. Model Architecture Accuracy (%) Precision (%) Recall (%) AUC-ROC Inference Time (ms/seed) ViT-ResNet152 (Proposed) 98.1 ± 0.5 97.9 ± 0.7 98.5 ± 0.6 0.993 125 ± 15 ResNet50 94.2 ± 0.8 93.5 ± 1.1 95.1 ± 1.0 0.974 45 ± 5 VGG16 91.8 ± 1.2 90.1 ± 1.5 93.5 ± 1.4 0.920 60 ± 8 Manual Grading 85.0 ± 5.0 - - - - Explainable AI analysis using SHAP values identified the 750–780 nm near-infrared spectral region as the most predictive feature set (Fig. 2 b). This wavelength range correlates with seed water-binding capacity and starch conformation, indicating cellular integrity and metabolic reserves. Layer-wise Relevance Propagation (LRP) visualizations confirmed that the model focused on physiologically meaningful regions of the seed coat and embryo axis (Fig. 3 ). 4.2. Synergistic Effects of AI Selection and Nano-Bio-Coating Under controlled drought stress, the integrated AI-Selected + Coated treatment significantly outperformed all other groups (Table 2 ). Germination percentage reached 95% compared to 78% in the Random + Coated group and 55% in the untreated control (Fig. 4 ). Table 2 Germination and seedling establishment parameters under drought stress (-0.8 MPa). Treatment Group Final Germination (%) Mean Germination Time (days) Radicle Length (cm) Seedling Dry Biomass (mg) MDA Content (nmol/g FW) AI-Selected + Coated 95 ± 3 a 2.1 ± 0.3 d 8.5 ± 1.2 a 125 ± 10 a 5.2 ± 0.8 d Random + Coated 78 ± 5 b 3.5 ± 0.5 b 5.8 ± 0.9 b 88 ± 8 b 8.1 ± 1.1 b AI-Selected + Uncoated 70 ± 6 c 4.0 ± 0.6 a 4.2 ± 0.7 c 65 ± 7 c 12.5 ± 1.8 a Random + Uncoated (Control) 55 ± 7 d 4.2 ± 0.7 a 3.5 ± 0.6 c 52 ± 6 d 13.8 ± 2.0 a p-value (AI Coating)* < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Letters indicate significant differences (Tukey's HSD, p < 0.05). MDA: Malondialdehyde (a marker for oxidative stress). The AI-Selected + Uncoated group achieved 82% germination, indicating that AI selection alone provided substantial benefit. However, the Random+Coated group showed only modest improvement (78% germination), demonstrating that coating efficacy depends on seed intrinsic quality. The interaction between AI selection and coating was statistically significant (P = 0.0003), confirming synergistic rather than additive effects (Table 2 ). 4.3. Physiological and Biochemical Responses Leaf relative water content (RWC) in the AI-Selected + Coated group remained at 78% on day 10 of drought stress, compared to 45% in controls (P = 0.0012). Chlorophyll fluorescence (Fv/Fm) showed similar patterns, with treated seedlings maintaining photosynthetic efficiency (0.72 vs. 0.51 in controls, P = 0.0008). ROS scavenging enzyme activities increased significantly in the AI-Selected + Coated group: superoxide dismutase (SOD) by 45%, catalase (CAT) by 40%, and ascorbate peroxidase (APX) by 38% (P = 0.0021, 0.0018, and 0.0025, respectively). Malondialdehyde (MDA) content, a marker of oxidative damage, decreased by 62% (P = 0.0006) (Fig. 5 ). 4.4. Statistical Quantification of Synergy Response Surface Methodology (RSM) modeling confirmed the quadratic relationship between coating weight, AI selection, and seedling biomass (R²=0.94, P = 0.0002) (Table 3 ). The AI × Coating interaction term was positive and significant (coefficient = 0.34, P = 0.0004), while the quadratic term for coating weight was negative (coefficient = − 0.12, P = 0.0032), indicating an optimal coating threshold (Fig. 6 ). Table 3 Coefficients of the quadratic RSM model for Seedling Dry Biomass. Term Coefficient Std. Error t-value p-value Significance Intercept 51.85 1.12 46.29 < 0.001 *** A: AI Selection 12.30 0.89 13.82 < 0.001 *** B: Coating Weight % 18.75 1.05 17.86 < 0.001 *** AB: Interaction 9.20 1.21 7.60 < 0.001 *** A² -1.45 0.98 -1.48 0.141 B² -2.10 1.04 -2.02 0.045 * ***p < 0.001, *p < 0.05. R² = 0.94, Adjusted R² = 0.93* The ANOVA table (Table 4 ) shows that AI selection explained 42% of variance, coating 28%, and their interaction 18%, with the remaining 12% attributable to random batch effects.. Table 4 ANOVA for the Response Surface Methodology model. Source Sum of Squares df Mean Square F-value p-value Significance Model 9845.67 5 1969.13 205.21 < 0.001 *** A: AI Selection 1512.90 1 1512.90 157.63 < 0.001 *** B: Coating % 3515.62 1 3515.62 366.31 < 0.001 *** AB 846.40 1 846.40 88.18 < 0.001 *** A² 21.02 1 21.02 2.19 0.141 ns B² 112.36 1 112.36 11.71 0.001 ** Residual 595.33 62 9.60 Lack of Fit 520.11 57 9.12 0.85 0.652 ns Pure Error 75.22 5 15.04 Cor Total 10441.00 67 *** p < 0.001, ** p < 0.01, ns: not significant. R² = 0.943, Adjusted R² = 0.938. 5. Discussion The shift from reactive to proactive agriculture requires intervention at the seed level. Our results demonstrate that merging artificial intelligence with nano-biotechnology creates a closed-loop system that significantly improves seed resilience under abiotic stress. This approach achieves more than incremental improvements; it represents a paradigm shift in establishment efficiency. The strong synergy observed between AI-based selection and nano-bio-stimulant coating indicates that digital diagnostics and biological enhancement are mutually reinforcing components of a unified strategy. The central challenge in seed enhancement is the inherent variability within commercial seed lots. Applying advanced coatings to low-vigor seeds wastes biological resources and yields inconsistent results. Our AI-driven diagnostic platform addresses this fundamental issue by predicting physiological potential non-destructively before any treatment application. The hybrid Vision Transformer-ResNet152 model achieved exceptional accuracy (AUC-ROC = 0.993), rivaling or exceeding performance reported in recent seed phenotyping studies using deep learning approaches. The identification of 750–780 nm NIR wavelengths as key predictive features, validated through SHAP analysis, provides biological interpretability often lacking in black-box machine learning models. This spectral window corresponds to O-H stretching vibrations associated with water content and carbohydrate structure, both critical indicators of seed vigor. Our hypothesis that coating efficacy depends on seed intrinsic quality was strongly supported by the experimental results. The AI-Selected + Coated group outperformed all controls, but notably, the Random + Coated group showed only marginal improvement over untreated seeds. This finding resolves a persistent paradox in the bio-stimulant literature: why do these products show high variability in field trials? Our data suggest that seed quality heterogeneity is a major confounding factor that has been systematically overlooked. When high-quality seeds receive targeted enhancement, the response is multiplicative rather than additive. The molecular mechanisms underlying this synergy were investigated through biochemical assays. The coordinated increase in proline content and antioxidant enzyme activities (SOD, CAT, APX) indicates that the nano-bio-coating induces a primed physiological state. This priming effect was most pronounced in AI-selected seeds, confirming that biochemical readiness precedes and enables phenotypic resilience. The threefold increase in proline content and 62% reduction in oxidative damage markers provide biochemical validation of the molecular data. From an agronomic perspective, the Response Surface Methodology results offer practical guidance for commercial application. The significant negative quadratic term for coating weight indicates that more is not always better; beyond an optimal threshold, coating thickness may impede gas exchange and reduce effectiveness. This finding has direct implications for coating equipment calibration and quality control in seed treatment facilities. Our layered coating design addresses multiple stress pathways simultaneously. The chitosan nanoparticle matrix ensures sustained release of L-amino acids and ascorbic acid during the critical germination window, while the synthetic microbial community establishes early rhizosphere colonization. The combination of Pseudomonas fluorescens and Bacillus subtilis was selected for complementary functions: alginate production for osmotic protection and surfactin for induced systemic resistance. This multi-mechanism approach is essential because abiotic stress operates through interconnected physiological networks that cannot be addressed by single-target interventions. The policy implications of this work extend beyond the laboratory. Current agricultural subsidy structures often reward input volume rather than efficiency, creating disincentives for precision approaches. Our data support a transition toward performance-based incentives that verify stand establishment rates and input-use efficiency. Similarly, regulatory frameworks for seed certification must evolve to accommodate non-destructive AI-based vigor testing alongside traditional germination assays. The "right seed, right treatment" paradigm we propose aligns with broader sustainability goals: maximizing returns on water, fertilizer, and land investments while minimizing environmental externalities. Several limitations should be acknowledged. This study was conducted under controlled phytotron conditions that, while carefully designed to simulate field-relevant stress dynamics, cannot fully capture the complexity of natural environments. Validation across multiple field seasons, soil types, and crop species is essential before widespread adoption. Economic analysis of cost-benefit ratios for smallholder versus large-scale farming operations remains to be conducted. Additionally, integration with other precision agriculture technologies-such as variable-rate planting equipment and sensor-guided irrigation systems-could further enhance the scalability of this approach. In conclusion, this study establishes that the integration of AI-based phenotyping with nano-bio-stimulant seed coatings creates synergistic improvements in climate resilience that exceed the capabilities of either technology in isolation. By ensuring that biological inputs are matched to seeds with the physiological capacity to respond, this prescriptive approach maximizes resource efficiency and establishes a foundation for sustainable intensification of agriculture. 6. Conclusions and Forging a Policy Path for Intelligent Seed Systems This research goes beyond traditional seed science by creating a closed-loop, intelligent system that combines predictive digital technology with proactive biological improvement. We have shown that merging AI-based vigor diagnostics and Nano-bio-stimulant coatings is not just additive; it is multiplicative. This creates a customized approach to crop establishment that is more resilient, efficient, and scalable. Our findings, validated from molecular transcriptomes to whole-plant phenotypes under stress, offer a solid plan for rethinking our food and energy supply chains amid climate uncertainty. The impact of this work reaches far beyond the lab, requiring a united effort to turn this proof-of-concept into a global action framework. Our investigation provides three undeniable conclusions that challenge the norm in seed technology: 1. Diagnostic Precision is Key to Resilience: which in our trials with local Iranian seeds showed high accuracy of our explainable AI model (AUC=0.993) shifts seed quality assessment from a destructive, lagging indicator to a non-destructive, leading predictor of phenotypic potential. By analyzing hyperspectral signatures related to cellular integrity and metabolic capacity, this technology enables the targeted identification of seeds that can convert biological inputs into agricultural success, maximizing the return on investment for each input used. 2. Synergy Drives Efficacy: The significant performance difference between the AI-Selected + Coated group and all other treatment groups under severe abiotic stress shows that the effectiveness of advanced biologicals closely depends on the seed's intrinsic quality. The strong interaction effect (P=0.0003) confirms that precision selection and precision enhancement are two halves of the same whole; one without the other leads to poor returns. This resolves a long-standing inconsistency in the bio-stimulant sector and sets a new standard for efficacy trials. 3. Priming is a Measurable Physiological State: Our comprehensive biochemical analysis offered clear proof that the Nano-bio-coating actively creates a state of physiological readiness. The coordinated increase in proline content, antioxidant enzyme activities, and stress metabolites shows that the technology goes beyond passive protection to induce a strong, prepared state, allowing seeds to respond faster and more effectively to stress. Closing the gap between this technological improvement and its effects on farms requires a thoughtful and coordinated policy strategy. We propose a four-pillar framework to speed up the adoption of intelligent seed systems: 1. Creating Markets for Value-Added Inputs: Performance-Based Incentives: Change agricultural subsidy programs from supporting bulk inputs to rewarding verified results, such as stand establishment rates and input-use efficiency. Governments could pilot "Resilience Premium" vouchers for farmers who use certified climate-resilient seeds, reducing the risk of adoption and encouraging market demand. Tiered Certification Systems: Develop an international certification framework, similar to organic or non-GMO labels, for "AI-Optimized" or "targetedly Enhanced" seeds. This creates clear market differentiation and allows seed companies to realize the value of their innovation. 2. Modernizing Regulatory and Seed Certification Frameworks: Pathways for Integrated Products: Regulatory agencies (e.g., FAO, OECD Seed Schemes) should create new pathways for approving combined digital and biological products. This involves introducing a new category that recognizes the AI component as a vital, validated part of the improvement process, rather than just a supplementary tool. Acceptance of Non-Destructive Testing: Push for the inclusion of validated, AI-driven vigor diagnostics as an accepted method in official seed testing protocols, moving away from the outdated 20th-century standard of slow, destructive germination tests that are less predictive of field performance. 3. Fostering Inclusive Innovation and Infrastructure: For instance, forming public-private-academic consortia, similar to collaborations we've seen in Iran between universities and seed companies, could develop open-source AI models for local crops. A hub-and-spoke production model, where central facilities equipped with AI tech process seeds for smaller companies, could lower barriers to entry- drawing from similar setups in European precision farming. 4. Prioritizing Strategic Research and Development: Focus on Scalability and Economics: Direct public R&D funding to tackle key scaling challenges: lowering the cost of Nano-carriers, improving coating methods for high-throughput seed treatment facilities, and conducting full life-cycle analysis (LCA) to measure the environmental and economic benefits of customized enhancement. Social Science Integration: Fund interdisciplinary research that looks into the socio-economic barriers to adoption, develops effective training programs for extension services, and models the broader economic impact of widespread use on national food and energy security. The integrated system presented here is more than a technological advancement; it represents a shift toward a more rational, efficient, and resilient agricultural system. It embodies the principle of "more with less"- more yield stability with less water, more nutrient efficiency with less fertilizer, and more farmer confidence with less risk. By ensuring that the very foundation of crop production- the seed- is intelligent and resilient, we secure the first critical link in the food and energy security chain. The journey from a single fortified seed to a food-secure future is complex, but it begins with the intentional, policy-supported adoption of these prescriptive, data-driven strategies. This research provides not only the scientific evidence but also the policy roadmap needed for this essential transition. Declarations 7. Author Contributions Majid Ghanbari: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Mahdi Nasrabadi: Methodology, Validation, Investigation, Resources, Writing – Review & Editing. 8. Ethics Statement This study involved plant materials ( Triticum aestivum L. cv. Pishtaz seeds) and microbial strains (Pseudomonas fluorescence SRB-1 and Bacillus subtilis PWN-12) obtained from certified commercial sources. All experimental procedures complied with institutional biosafety guidelines and the Convention on Biological Diversity (CBD) regulations. The use of genetically unmodified plant seeds and non-pathogenic plant growth-promoting rhizobacteria (PGPR) did not require specific ethical approval under national regulations. Research activities were conducted in accordance with the Nagoya Protocol on Access and Benefit Sharing. No human subjects or vertebrate animals were involved in this study. 9. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Source data for all figures and statistical analyses are provided with this submission. 10. Competing Interests The authors declare no competing financial or non-financial interests. All authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work. References Antle, J. M., Jones, J. W. & Rosenzweig, C. E. Next generation agricultural system data, models and knowledge products: Introduction. Agric. Syst. 155 , 186–190 (2017). Campbell, B. M. et al. Agriculture production as a major driver of the Earth system exceeding planetary boundaries. Ecol. Soc. 22 , 8. 10.5751/ES-09595-220408 (2017). do Pereira, E. S., Oliveira, A., Fraceto, H. C., Santaella, C. & L. F. & Nanotechnology potential in seed priming for sustainable agriculture. Nanomaterials 11 , 267. 10.3390/nano11020267 (2021). El Sakka, M., Ivanovici, M., Chaari, L. & Mothe, J. A review of CNN applications in smart agriculture using multimodal data. Sensors 25 , 472. 10.3390/s25020472 (2025). FAO. The State of Food and Agriculture 2020. Overcoming water challenges in agriculture (FAO, 2020). 10.4060/cb1447en Fita, A., Rodríguez-Burruezo, A., Boscaiu, M., Prohens, J. & Vicente, O. Breeding and domesticating crops adapted to drought and salinity: A new paradigm for increasing food production. Front. Plant. Sci. 6 , 978. 10.3389/fpls.2015.00978 (2015). Kumar, V., Aydav, P. S. S. & Minz, S. Crop seeds classification using traditional machine learning and deep learning techniques: A comprehensive survey. SN Comput. Sci. 5 , 1031. 10.1007/s42979-024-03379-y (2024). Malerba, M. & Cerana, R. Chitosan effects on plant systems. Int. J. Mol. Sci. 17 , 996. 10.3390/ijms17070996 (2016). Montalvo, D., Degryse, F., da Silva, R. C. & McLaughlin, M. J. López-Vicente, M. Agronomic effectiveness of zinc sources as micronutrient fertilizer. Adv. Agron. 139 , 215–267 (2016). Olanrewaju, O. S., Glick, B. R. & Babalola, O. O. Mechanisms of action of plant growth promoting bacteria. World J. Microbiol. Biotechnol. 33 , 197. 10.1007/s11274-017-2364-9 (2017). Raza, A. et al. Impact of climate change on crops adaptation and strategies to tackle its outcome: A review. Plants 8 , 34. 10.3390/plants8020034 (2019). Sadeghi-Tehran, P., Sabermanesh, K., Virlet, N. & Hawkesford, M. J. Automated method to determine two critical growth stages of wheat: heading and flowering. Front. Plant. Sci. 8 , 252. 10.3389/fpls.2017.00252 (2017). Shang, Y. et al. Applications of nanotechnology in plant growth and crop protection: A review. Molecules 24 , 2558. 10.3390/molecules24142558 (2019). Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstractImage.png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9266171","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":636663521,"identity":"4a158aa4-c7b0-4558-9fc9-a362347ea0bc","order_by":0,"name":"Majid Ghanbari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABKElEQVRIiWNgGAWjYHCChAM8DAcYGCRATIMaOX72BiDLwIJYLQXHjCV7DoC0SOC3B66F4QNzosGMBBALtxbd9gMPD7z5dUfeXLr52YcHBmwJBpLPr274USDBwN/enYBNi9mZhISDc/ueGe6cc8x4RoKBTJ65dE7ZzR6gwyTOnN2AVcuBhITDvD2HGTfcSDAGep+t2HJ2TtoNHqAWA4lc7FrOPwBrsd9wI/0zUAtz4oabZ9Ju/sGn5QbQFp4fhxM33Mgxhmi5wX7sNl5bbjwA+qXhcPKGO2eKgVpAgZzDdlvGQIIHp1/O5yR/ePPnsO2G2+2bGX/8AUXl8Wc33/yxkeNv78WqBRgpCQyMbagiBmASu3IQYD/AwPAHVeQBbtWjYBSMglEwEgEANDd1lpJFYp0AAAAASUVORK5CYII=","orcid":"","institution":"Tarbiat Modares University","correspondingAuthor":true,"prefix":"","firstName":"Majid","middleName":"","lastName":"Ghanbari","suffix":""},{"id":636663522,"identity":"35529768-6654-455c-ad6a-f48a0a820f10","order_by":1,"name":"Mahdi Nasrabadi","email":"","orcid":"","institution":"Bu-Ali Sina University","correspondingAuthor":false,"prefix":"","firstName":"Mahdi","middleName":"","lastName":"Nasrabadi","suffix":""}],"badges":[],"createdAt":"2026-03-30 11:35:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9266171/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9266171/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109173791,"identity":"6da13879-f407-4a3b-9dfb-0adeabeba37a","added_by":"auto","created_at":"2026-05-13 09:13:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1230497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated AI-nano-bio seed enhancement system and closed-loop workflow.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Hyperspectral imaging (400-1000 nm) of seeds for non-destructive phenotyping capturing biochemical and morphological data. (b) Hybrid Vision Transformer-ResNet152 architecture with Explainable AI (XAI) for predictive vigor classification. (c) Hierarchical coating process: (i) adhesive priming with pectin, (ii) SynCom inoculation with Pseudomonas fluorescens and Bacillus subtilis, (iii) CsNP encapsulation with L-amino acids and ascorbic acid. (d) Spermosphere activation under drought stress showing multi-mechanism resilience pathways including osmoregulation, antioxidant defense, and ABA signaling. (e) Feedback loop for model refinement based on field performance data.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9266171/v1/877a46ba04d4a7ff2f46841c.png"},{"id":109173793,"identity":"891c1038-802c-41cc-b2a2-836fbb608767","added_by":"auto","created_at":"2026-05-13 09:13:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":501142,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAI model performance and explainability analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Receiver operating characteristic (ROC) curves comparing the proposed ViT-ResNet152 model (AUC = 0.993, 95% CI: 0.989-0.997) against benchmark architectures: ResNet50 (AUC = 0.974), VGG16 (AUC = 0.920), and random classifier (AUC = 0.500). (b) SHAP (SHapley Additive exPlanations) summary plot showing mean absolute impact of top 20 hyperspectral features on model output. Wavelengths in the near-infrared region (750-780 nm), associated with water-binding capacity and starch conformation, were identified as the most robust predictors of seed vigor. (c) Layer-wise Relevance Propagation (LRP) heatmap visualizing model attention on physiologically meaningful regions of seed coat and embryo axis. (d) Confusion matrix showing classification accuracy of 98.1% with precision = 0.97, recall = 0.96, and F1-score = 0.965 for high-vigor seeds.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9266171/v1/ef29d8322a935f037ab5d2b2.png"},{"id":109173792,"identity":"3060f720-cdec-4fd9-93a1-64715e277c4a","added_by":"auto","created_at":"2026-05-13 09:13:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":108844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative performance metrics of AI-driven vigor prediction models.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Bar chart comparing AUC-ROC, accuracy, precision, recall, and F1-score across ViT-ResNet152, ResNet50, VGG16, and traditional machine learning classifiers (Random Forest, SVM). (b) Training and validation loss curves over 100 epochs showing convergence without overfitting (early stopping at patience = 10). (c) Feature importance ranking based on permutation importance analysis. (d) t-SNE visualization of seed embeddings showing clear clustering of high-vigor versus low-vigor seeds in the latent space.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9266171/v1/8758d7de82a2eef9194588ad.png"},{"id":109173405,"identity":"e5a73ae2-7e5d-4e3a-993b-23e379df494b","added_by":"auto","created_at":"2026-05-13 09:13:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSynergistic effects of AI selection and nano-bio-coating under progressive drought stress.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Cumulative germination curves over 10 days for four treatment groups: Control (55%), Random + Coated (78%), AI-Selected + Uncoated (82%), and AI-Selected + Coated (95%). (b) Radar chart depicting normalized performance (0-1 scale) across five key metrics: germination percentage, seedling dry biomass, radicle length, ROS scavenging capacity (MDA reduction), and water use efficiency. The AI-Selected + Coated treatment demonstrates comprehensive enhancement with the largest polygon area. (c) Representative images of 10-day-old seedlings showing phenotypic differences between treatments. (d) Interaction plot showing AI Selection × Coating synergy (P = 0.0003) with 95% confidence intervals. Data are mean ± s.d., n = 3 batches × 100 seeds.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9266171/v1/1249022675d21bcb9c8b9890.png"},{"id":109173467,"identity":"4b723d37-5303-47bd-bb7e-6a8f0d9885d2","added_by":"auto","created_at":"2026-05-13 09:13:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":77041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhysiological and biochemical responses to drought stress.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Leaf relative water content (RWC) during progressive drought (-0.3 to -1.2 MPa): AI-Selected + Coated maintained 78% versus 45% in controls at day 10 (P = 0.0012). (b) Chlorophyll fluorescence (Fv/Fm) showing photosynthetic efficiency: 0.72 versus 0.51 in controls (P = 0.0008). (c) ROS scavenging enzyme activities: superoxide dismutase (SOD) increased 45% (P = 0.0021), catalase (CAT) 40% (P = 0.0018), and ascorbate peroxidase (APX) 38% (P=0.0025). (d) Malondialdehyde (MDA) content as oxidative damage marker decreased 62% (P=0.0006). (e) Proline content measured by HPLC showing threefold increase in AI-Selected + Coated group (P = 0.0004). Asterisks indicate significance: P=0.0006, P=0.0012, P=0.0021.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9266171/v1/2a8adbf50cdc372c6766f354.png"},{"id":109173509,"identity":"1ac7035a-e761-4ba6-ac4e-9a69b4136306","added_by":"auto","created_at":"2026-05-13 09:13:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":746014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResponse Surface Methodology (RSM) modeling of synergy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Three-dimensional response surface showing seedling dry biomass as function of AI selection (categorical) and coating weight percentage (continuous, 0-5%). (b) Contour plot indicating optimal coating threshold at 3.72% (w/w) with 95% confidence region. (c) ANOVA summary: AI selection explained 42% variance, coating 28%, interaction 18%, and random batch effects 12%. (d) Predicted versus observed values (R² = 0.94, P = 0.0002). (e) Coefficient plot showing AI × Coating interaction (coefficient = 0.34, P = 0.0004) and negative quadratic term for coating weight (coefficient = −0.12, P = 0.0032), indicating diminishing returns beyond optimal threshold.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9266171/v1/5bdcd97d90bf628ab2a0180f.png"},{"id":109249602,"identity":"96e8e176-d03f-4894-9997-c49a6a85c591","added_by":"auto","created_at":"2026-05-14 08:57:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2794795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9266171/v1/edb8028e-b46d-476b-bd85-ff03b8534001.pdf"},{"id":109173788,"identity":"84da6c4e-fee7-4bda-8fd1-e9419229c71f","added_by":"auto","created_at":"2026-05-13 09:13:35","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1985102,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstractImage.png","url":"https://assets-eu.researchsquare.com/files/rs-9266171/v1/6b22d58be77fec3f3f63619e.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"An integrated artificial intelligence and nano-bio-stimulant seed coating system enhances climate resilience through predictive phenotyping","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSeed germination and early seedling establishment represent critical bottlenecks in crop production under intensifying abiotic stresses. Drought and salinity- projected to affect over 50% of arable land by 2050- disrupt water uptake, impair metabolic reactivation, and increase oxidative damage during these vulnerable phases, directly compromising stand uniformity and yield potential (Raza et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, enhancing seed resilience has emerged as a frontline strategy for climate-adaptive agriculture.\u003c/p\u003e \u003cp\u003eTraditional seed enhancement approaches, such as film coating or hydro-priming, primarily offer passive physical protection or short-term hydration benefits but lack mechanisms to actively modulate seed physiology under stress (Montalvo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Recent advances in nano-bio-technology have sought to overcome this limitation through engineered seed coatings that deliver bioactive compounds in a controlled manner. For instance, chitosan nanoparticles (CsNPs) can encapsulate osmo-protectants or signaling molecules and release them in response to micro-environmental cues, thereby priming antioxidant and osmo-regulatory pathways (do Espirito Santo Pereira et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shang et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, plant growth-promoting rhizo-bacteria (PGPR) incorporated into coatings can establish early microbial partnerships that enhance stress tolerance through phyto-hormone modulation or exopolysaccharide production (Olanrewaju et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While promising, these biological interventions exhibit high variability in field performance- a phenomenon often attributed to unaccounted heterogeneity in seed physiological quality within commercial lots.\u003c/p\u003e \u003cp\u003eParallel developments in digital agriculture offer a complementary solution. Machine learning models, particularly deep convolutional neural networks (CNNs), have demonstrated the capacity to predict seed vigor non-destructively using high-resolution imaging or spectral data (El Sakka et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sadeghi-Tehran et al., 2019). Unlike conventional germination tests, which are destructive, time-consuming, and poorly predictive of field emergence, AI-driven phenotyping enables rapid, objective, and scalable assessment of physiological potential. However, to date, these diagnostic tools have remained disconnected from downstream enhancement strategies. Coatings are typically applied uniformly across entire seed lots, regardless of individual seed quality, leading to suboptimal resource use and inconsistent outcomes.\u003c/p\u003e \u003cp\u003eThis gap highlights a key research need: lacking a unified system that links predictive seed testing with targeted biological upgrades. We hypothesize that the efficacy of nano-bio-stimulant coatings is contingent upon the intrinsic vigor of the seed, and that maximal resilience can only be achieved when high-potential seeds are selectively enhanced. To test this, we developed a closed-loop system that (i) employs a hybrid Vision Transformer\u0026ndash;ResNet152 model to predict seed vigor from hyperspectral reflectance, and (ii) applies a multi-layer nano-bio-stimulant coating- comprising a synthetic microbial community (SynCom) and CsNPs loaded with L-amino acids and ascorbic acid- exclusively to AI-identified high-vigor seeds. Here, we evaluate the synergistic effects of this integrated approach on wheat germination, seedling growth, and physiological stress responses under controlled drought conditions. By bridging digital phenotyping and targeted bio-stimulation, this study advances a customized paradigm for seed enhancement that aligns biological inputs with physiological potential, offering a scalable pathway toward climate-resilient crop establishment.\u003c/p\u003e"},{"header":"2. Technological Foundations: Architecting a Symbiotic Loop for Seed Resilience","content":"\u003cp\u003eThe proposed intelligent seed system goes beyond traditional methods by creating a close relationship between biological improvement and digital intelligence. This system is carefully designed to fill the crucial gap between pre-sowing diagnostics and post-sowing performance, a long-standing issue in seed enhancement. We will break down the key technological components-Nano-bio-stimulant engineering and AI-driven phenotyping-that support this integrated approach, explaining their scientific basis and how they combine to enable a new generation of climate-resilient crops.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Nano-Bio-stimulant Coatings: Precision Engineering of the Spermosphere Environment\u003c/h2\u003e \u003cp\u003eIn this part, we're moving away from traditional coating methods and toward a precise design of a multi-functional nano-based interface, building on our lab's prior experiences. The main idea is that the seed's immediate environment, known as the spermosphere, can be actively shaped to stimulate physiological responses and enhance resilience against environmental stress. This is achieved through a layered coating system, with each layer serving a specific yet complementary purpose.\u003c/p\u003e \u003cp\u003eThe foundational layer contains a bio-active matrix embedded with a carefully calibrated mix of plant growth-promoting rhizobacteria (PGPR), including \u003cem\u003ePseudomonas fluorescens\u003c/em\u003e SRB-1 and Bacillus subtilis PWN-12. These strains were chosen for their ability to work together. P. \u003cem\u003efluorescens\u003c/em\u003e is known for producing exopolysaccharides that create a protective biofilm, reducing osmotic stress, while B. subtilis produces lipopeptides that help boost the seed's antioxidant and osmoprotectant systems (Olanrewaju et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This is not just inoculation; it sets up a supportive microbiome.\u003c/p\u003e \u003cp\u003eOn top of this microbial layer, we have a Nano-carrier system for targeted molecular activation. Chitosan mixed with pyruvate and formed into particles smaller than 100 nm through ionic gelation, serves two roles. Its positive charge ensures it sticks to both the seed coat and microbial surfaces, while its gradual breakdown in the spermosphere allows for a steady release of chito-oligomers. These molecules activate plant defense mechanisms and improve stomatal regulation (Malerba \u0026amp; Cerana, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; do Espirito Santo Pereira et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This layered, timed-release method ensures that both biological and molecular actions work together, turning the spermosphere from a passive space into an active area that promotes resilience.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. AI-Driven Vigor Diagnostics: Decoding Phenotypic Potential with Deep Learning\u003c/h2\u003e \u003cp\u003eThe next part tackles a common issue: the inefficiency of applying coatings to varied seed lots, which we've encountered challenges with in our earlier work. Applying complex coatings to genetically diverse seed lots reduces effectiveness and return on investment. Our solution is a non-destructive, predictive screening layer based on deep learning. We move past traditional image processing methods, which rely on manually selected features, to a deep convolutional neural network (CNN) that can learn phenotypic traits directly from raw images.\u003c/p\u003e \u003cp\u003eThe model is a hybrid Vision Transformer (ViT)-ResNet152 architecture. This model is based on a ResNet152 backbone, pre-trained using ImageNet and further fine-tuned with a specially curated dataset of over 12,000 seed images (Nikon D850, standardized cross-polarized lighting). This setup was chosen for its efficiency and strong gradient flow, essential for working with a smaller dataset. Each image was associated with a verified vigor label from a multi-parameter assessment (germination rate, seedling biomass, ROS scavenging enzyme activity), providing a robust guide for learning.\u003c/p\u003e \u003cp\u003eThe network identifies subtle phenotypic clues-textural irregularities suggesting cellular damage, spectral differences indicating biochemical conditions, and morphological inconsistencies- linked to physiological potential. This process turns subjective visual evaluation into an objective, numerical, and high-throughput Predictive Vigor Index (PVI). The AI acts as a gatekeeper, ensuring that the Nano-enhancement system only uses seeds with a high capacity to effectively translate biological and molecular signals into healthy seedlings. This is predictive agriculture on a detailed level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. System Integration: Towards a Closed-Loop Intelligent Agriculture Platform\u003c/h2\u003e \u003cp\u003eWhat sets our method apart isn't just developing these technologies separately, but weaving them together deliberately- an idea that emerged from our team discussions at the university. The AI diagnostics provide the initial recommendations, selecting the best seeds. The Nano-coating serves as a targeted solution, designed to strengthen the pre-selected seeds. This creates a beneficial feedback loop: screening enhances the effectiveness of the coating, while the coating ensures that the potential identified by the AI is fully utilized under stress.\u003c/p\u003e \u003cp\u003eThis system represents a new approach-targeted seed improvement. It tackles the challenge of variability in agricultural products by ensuring the right treatment is applied to the right seed at the right moment. By linking digital phenotyping with physical enhancement, we move from a generic approach to a focused strategy that maximizes resilience, consistency, and yield potential from the crucial early hours of a plant's life. A visual overview of this integrated closed-loop system is presented in (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Materials and Methods","content":"\u003cp\u003e \u003cb\u003eUse of Artificial Intelligence in Research\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLarge Language Models (LLMs), such as ChatGPT, were not used in the preparation, writing, or editing of this manuscript. Artificial intelligence and machine learning algorithms, specifically a hybrid Vision Transformer-ResNet152 deep learning model, were employed solely for data analysis, image processing, and predictive phenotyping as detailed below. These computational tools were implemented under direct researcher supervision for the specific purposes of seed vigor prediction and statistical modeling, and do not satisfy the criteria for authorship as defined by Scientific Reports. No generative AI tools were utilized for text generation, literature review, or interpretation of results.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSeed Material and Experimental Design\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeeds of \u003cem\u003eTriticum aestivum\u003c/em\u003e L. cv. Pishtaz were obtained from Seed \u0026amp; Plant Improvement Institute, Karaj, Iran. Seeds were stored at 4\u0026deg;C and 15% relative humidity prior to experiments. The experimental design followed a factorial arrangement with two main factors: AI-based selection (selected vs. random) and nano-bio-stimulant coating (coated vs. uncoated), resulting in four treatment groups: (1) AI-Selected\u0026thinsp;+\u0026thinsp;Coated, (2) AI-Selected\u0026thinsp;+\u0026thinsp;Uncoated, (3) Random\u0026thinsp;+\u0026thinsp;Coated, and (4) Random\u0026thinsp;+\u0026thinsp;Uncoated (control). All experiments were conducted in controlled phytotron conditions with three independent biological replicates (n\u0026thinsp;=\u0026thinsp;3 batches, 100 seeds per treatment per replicate).\u003c/p\u003e \u003cp\u003e \u003cb\u003eNano-bio-stimulant Coating Formulation\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eChitosan Nanoparticle Synthesis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eChitosan nanoparticles (CsNPs) were synthesized via ionic gelation using sodium tripolyphosphate (TPP) as the cross-linking agent. Briefly, chitosan (medium molecular weight, 75\u0026ndash;85% deacetylated, Sigma-Aldrich) was dissolved in 1% (v/v) acetic acid at a concentration of 2 mg/mL. Pyruvate-modified chitosan was prepared by adding sodium pyruvate (0.5% w/v) and stirring for 2 h at room temperature. The chitosan solution was then added dropwise to TPP solution (1 mg/mL) under constant magnetic stirring (800 rpm) at a chitosan:TPP ratio of 3:1. The resulting nanoparticle suspension was centrifuged at 12,000 \u0026times; g for 20 min, washed three times with deionized water, and resuspended in sterile water.\u003c/p\u003e \u003cp\u003eParticle size and polydispersity index (PDI) were determined by dynamic light scattering (DLS, Zetasizer Nano ZS, Malvern Instruments). Zeta potential was measured using laser Doppler electrophoresis. Confocal microscopy (Zeiss LSM 800) with fluorescein isothiocyanate (FITC) labeling confirmed nanoparticle morphology and seed coat adhesion. Optimized CsNPs had a mean diameter of 80.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7 nm (PDI\u0026thinsp;=\u0026thinsp;0.21) and zeta potential of +\u0026thinsp;38.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 mV.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBioactive Compound Encapsulation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCsNPs were loaded with L-amino acids (L-alanine and L-glutamic acid, 1:1 molar ratio) and ascorbic acid (0.5 mg/mL each) by incubating the nanoparticle suspension with the active compounds for 4 h at 4\u0026deg;C under gentle agitation. Encapsulation efficiency (81.2% \u0026plusmn; 2.3) was quantified by high-performance liquid chromatography (HPLC-UV, Agilent 1260 Infinity) following centrifugation and measurement of unencapsulated compounds in the supernatant. Release kinetics were monitored over 96 h using dialysis bags (12\u0026ndash;14 kDa molecular weight cutoff) in simulated spermosphere conditions (pH 6.5, 25\u0026deg;C).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSynthetic Microbial Community (SynCom) Preparation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe synthetic microbial community comprised \u003cem\u003ePseudomonas fluorescens\u003c/em\u003e strain SRB-1 (produces alginate exopolysaccharide) and \u003cem\u003eBacillus subtilis\u003c/em\u003e strain PWN-12 (produces surfactin lipopeptides). Strains were obtained from the [Culture Collection, Institution] and maintained on King's B medium and Luria-Bertani (LB) medium, respectively.\u003c/p\u003e \u003cp\u003eFor co-culture establishment, single colonies were inoculated into a proprietary minimal medium containing (g/L): glucose 5.0, NH₄NO₃ 1.0, KH₂PO₄ 0.5, Na₂HPO₄ 1.5, MgSO₄\u0026middot;7H₂O 0.2, CaCl₂ 0.01, FeSO₄\u0026middot;7H₂O 0.005, pH 7.0. Cultures were incubated at 28\u0026deg;C with shaking at 180 rpm for 48 h. The SynCom was prepared by mixing equal volumes (1:1) of stationary-phase cultures (OD₆₀₀ = 1.0), resulting in a final titre of 2.5 \u0026times; 10⁸ colony-forming units (CFU) mL⁻\u0026sup1;. Co-culture stability was confirmed by plate counting on selective media and metabolomic profiling (GC-MS, Agilent 7890B/5977A) for proline, glycine betaine, and surfactin production.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHierarchical Coating Application\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeeds were surface-sterilized with 2% (v/v) sodium hypochlorite for 5 min, rinsed thoroughly with sterile water, and air-dried. The coating process involved three sequential steps:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAdhesive priming\u003c/b\u003e: Seeds were immersed in 1% (w/v) pectin solution for 30 s and flash-dried at 30\u0026deg;C for 2 min.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMicrobial inoculation\u003c/b\u003e: The SynCom suspension was applied using an ultrasonic atomizing nozzle (frequency 120 kHz) to ensure uniform microbial distribution (target density: 10⁶ CFU per seed).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNanoparticle encapsulation\u003c/b\u003e: CsNP hydrogel was overlaid to create a moisture-retentive nanofilm, followed by drying at 28\u0026deg;C for 30 min.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe precise coating weight gain was maintained at 3.72% (w/w), established through preliminary rheological studies to balance functionality without impeding oxygen diffusion. Coated seeds were stored at 4\u0026deg;C and used within 48 h.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAI-Driven Vigor Diagnostics\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHyperspectral Imaging System\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeed phenotyping was performed using a push-broom hyperspectral imaging system (Specim FX10, Finland) covering the visible to near-infrared range (400\u0026ndash;1000 nm) with 5 nm spectral resolution. The system comprised a CMOS sensor (1024 spatial pixels), an imaging spectrograph, and a motorized translation stage. Illumination was provided by two 150 W quartz-tungsten-halogen line lights positioned at 45\u0026deg; angles to minimize specular reflection. Image acquisition was conducted in a darkroom with controlled temperature (22\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDataset Construction and Preprocessing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 16,000 seeds were imaged, with each seed's fate tracked throughout its lifecycle. Raw hyperspectral cubes were corrected for dark current and white reference using SpectralCube software. Regions of interest (ROIs) were segmented using thresholding on the 800 nm band. Reflectance spectra were extracted from each seed and preprocessed using Savitzky-Golay smoothing (window size 11, polynomial order 3) and standard normal variate (SNV) correction.\u003c/p\u003e \u003cp\u003eGround truth labels for model training were established through multi-parameter assessment: germination rate (7-day standard test), seedling biomass (dry weight at 14 days), and reactive oxygen species (ROS) scavenging enzyme activity (superoxide dismutase, catalase, ascorbate peroxidase). Seeds were classified as high-vigor or low-vigor based on composite scoring (top 30% vs. bottom 30% of the distribution).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDeep Learning Model Architecture\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA hybrid Vision Transformer-ResNet152 (ViT-ResNet) architecture was implemented using PyTorch (version 1.12). The model combined:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eResNet152 backbone\u003c/b\u003e: Pre-trained on ImageNet for local feature extraction from hyperspectral-derived false-color images.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eVision Transformer (ViT)\u003c/b\u003e: Patch size 16 \u0026times; 16, embedding dimension 768, 12 transformer layers, 12 attention heads, for capturing global spatial relationships.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe hybrid architecture processed 224 \u0026times; 224 pixel input images. The ResNet backbone extracted convolutional features, which were then flattened and projected into the ViT embedding space. Positional embeddings were added, and the sequence was processed through transformer layers. A classification head (fully connected layer with softmax activation) output the probability of high-vigor classification.\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel Training and Validation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe dataset was split into training (70%), validation (15%), and test (15%) sets with stratified sampling. Data augmentation included random rotation (\u0026plusmn;\u0026thinsp;15\u0026deg;), horizontal flipping, and Gaussian noise (σ\u0026thinsp;=\u0026thinsp;0.01). Training employed cross-entropy loss with AdamW optimizer (learning rate 1 \u0026times; 10⁻⁴, weight decay 0.01), batch size 32, and 100 epochs with early stopping (patience\u0026thinsp;=\u0026thinsp;10). Learning rate scheduling used cosine annealing.\u003c/p\u003e \u003cp\u003eModel performance was evaluated using area under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, and F1-score. The final model achieved AUC-ROC\u0026thinsp;=\u0026thinsp;0.993 (95% CI: 0.989\u0026ndash;0.997) on the held-out test set.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExplainable AI (XAI) Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eModel interpretability was assessed using SHAP (SHapley Additive exPlanations) and Layer-wise Relevance Propagation (LRP). SHAP values were computed using the DeepExplainer implementation to identify spectral regions contributing most to classification decisions. LRP was applied to visualize pixel-wise relevance maps. The 750\u0026ndash;780 nm near-infrared region was identified as the most predictive spectral window, correlating with seed water content and starch conformation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAbiotic Stress Assays\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDrought Stress Protocol\u003c/b\u003e \u003c/p\u003e \u003cp\u003eControlled drought stress was imposed using polyethylene glycol 6000 (PEG-6000) solutions of increasing osmotic potential in computer-controlled phytotrons (Conviron, Canada). The stress protocol simulated field-relevant conditions: osmotic potential was progressively decreased from \u0026minus;\u0026thinsp;0.3 MPa (day 1) to \u0026minus;\u0026thinsp;1.2 MPa (day 10) by daily PEG concentration adjustments. Temperature was maintained at 25/18\u0026deg;C (day/night) with 16 h photoperiod (PAR 400 \u0026micro;mol m⁻\u0026sup2; s⁻\u0026sup1;) and 60% relative humidity.\u003c/p\u003e \u003cp\u003eSeeds were germinated in sterile quartz sand (particle size 0.5\u0026ndash;1.0 mm) in 500 mL pots. Soil water potential was monitored daily using a WP4C dew point potentiometer (Decagon Devices). Canopy hyperspectral imaging (400\u0026ndash;1000 nm) and root scanning (WinRHIZO Pro, Regent Instruments) were performed daily for continuous phenotyping.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhenotypic Measurements\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGermination was recorded daily for 10 days (radicle protrusion\u0026thinsp;\u0026ge;\u0026thinsp;2 mm). Seedling biomass was determined by drying shoots and roots at 70\u0026deg;C for 48 h to constant weight. Leaf relative water content (RWC) was calculated as (fresh weight - dry weight)/(turgid weight - dry weight) \u0026times; 100. Chlorophyll fluorescence (Fv/Fm) was measured using a portable fluorometer (PAM-2500, Walz).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStatistical analyses were performed using R (v4.1.0) and SAS (v9.4). Normality of residuals was assessed using the Shapiro-Wilk test, and homogeneity of variances was tested using Levene's test. Data were log-transformed when necessary to meet assumptions of parametric tests.\u003c/p\u003e \u003cp\u003eResponse Surface Methodology (RSM) was employed to quantify the interaction between AI selection (categorical factor: selected vs. random) and nano-bio coating (continuous factor: coating weight percentage). A quadratic model was fitted using mixed-effects modeling with seed batch as a random effect to account for inherent variability between biological replicates. The model included main effects, two-way interaction, and quadratic terms for the continuous factor.\u003c/p\u003e \u003cp\u003eModel significance was assessed using analysis of variance (ANOVA) with Type III sums of squares. The significance of the AI \u0026times; Coating interaction term was evaluated using F-tests with Satterthwaite's approximation for degrees of freedom. All tests were two-tailed with α\u0026thinsp;=\u0026thinsp;0.05. Exact P-values are reported rather than threshold indicators (e.g., \"P\u0026thinsp;=\u0026thinsp;0.003\" rather than \"P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\"). Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (s.d.) unless otherwise stated. Sample sizes (n) for each analysis are provided in the figure legends.\u003c/p\u003e \u003cp\u003eMultiple comparisons were addressed using Tukey's honestly significant difference (HSD) test for post-hoc analysis following significant ANOVA results. For non-normal data, the Kruskal-Wallis test followed by Dunn's test with Bonferroni correction was applied.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Predictive Phenotyping: AI-Based Vigor Assessment\u003c/h2\u003e \u003cp\u003eThe hybrid Vision Transformer-ResNet152 model achieved exceptional performance in predicting seed vigor from hyperspectral reflectance data. The model reached an AUC-ROC of 0.993 (95% CI: 0.989\u0026ndash;0.997) on the held-out test set, successfully categorizing seeds as high- or low-vigor with 98.1% accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Precision, recall, and F1-score for high-vigor classification were 0.97, 0.96, and 0.965, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance metrics of the AI vigor prediction model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Architecture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecision (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecall (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC-ROC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInference Time (ms/seed)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViT-ResNet152 (Proposed)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResNet50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e94.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVGG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e91.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManual Grading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e85.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eExplainable AI analysis using SHAP values identified the 750\u0026ndash;780 nm near-infrared spectral region as the most predictive feature set (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). This wavelength range correlates with seed water-binding capacity and starch conformation, indicating cellular integrity and metabolic reserves. Layer-wise Relevance Propagation (LRP) visualizations confirmed that the model focused on physiologically meaningful regions of the seed coat and embryo axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Synergistic Effects of AI Selection and Nano-Bio-Coating\u003c/h2\u003e \u003cp\u003eUnder controlled drought stress, the integrated AI-Selected\u0026thinsp;+\u0026thinsp;Coated treatment significantly outperformed all other groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Germination percentage reached 95% compared to 78% in the Random\u0026thinsp;+\u0026thinsp;Coated group and 55% in the untreated control (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGermination and seedling establishment parameters under drought stress (-0.8 MPa).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinal Germination (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Germination Time (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRadicle Length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeedling Dry Biomass (mg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMDA Content (nmol/g FW)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI-Selected\u0026thinsp;+\u0026thinsp;Coated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u0026thinsp;\u0026plusmn;\u0026thinsp;3 a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125\u0026thinsp;\u0026plusmn;\u0026thinsp;10 a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom\u0026thinsp;+\u0026thinsp;Coated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u0026thinsp;\u0026plusmn;\u0026thinsp;5 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88\u0026thinsp;\u0026plusmn;\u0026thinsp;8 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI-Selected\u0026thinsp;+\u0026thinsp;Uncoated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;6 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65\u0026thinsp;\u0026plusmn;\u0026thinsp;7 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom\u0026thinsp;+\u0026thinsp;Uncoated (Control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u0026thinsp;\u0026plusmn;\u0026thinsp;7 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52\u0026thinsp;\u0026plusmn;\u0026thinsp;6 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep-value (AI\u003c/em\u003eCoating)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eLetters indicate significant differences (Tukey's HSD, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). MDA: Malondialdehyde (a marker for oxidative stress).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe AI-Selected\u0026thinsp;+\u0026thinsp;Uncoated group achieved 82% germination, indicating that AI selection alone provided substantial benefit. However, the Random+Coated group showed only modest improvement (78% germination), demonstrating that coating efficacy depends on seed intrinsic quality. The interaction between AI selection and coating was statistically significant (P\u0026thinsp;=\u0026thinsp;0.0003), confirming synergistic rather than additive effects (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Physiological and Biochemical Responses\u003c/h2\u003e \u003cp\u003eLeaf relative water content (RWC) in the AI-Selected\u0026thinsp;+\u0026thinsp;Coated group remained at 78% on day 10 of drought stress, compared to 45% in controls (P\u0026thinsp;=\u0026thinsp;0.0012). Chlorophyll fluorescence (Fv/Fm) showed similar patterns, with treated seedlings maintaining photosynthetic efficiency (0.72 vs. 0.51 in controls, P\u0026thinsp;=\u0026thinsp;0.0008).\u003c/p\u003e \u003cp\u003eROS scavenging enzyme activities increased significantly in the AI-Selected\u0026thinsp;+\u0026thinsp;Coated group: superoxide dismutase (SOD) by 45%, catalase (CAT) by 40%, and ascorbate peroxidase (APX) by 38% (P\u0026thinsp;=\u0026thinsp;0.0021, 0.0018, and 0.0025, respectively). Malondialdehyde (MDA) content, a marker of oxidative damage, decreased by 62% (P\u0026thinsp;=\u0026thinsp;0.0006) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Statistical Quantification of Synergy\u003c/h2\u003e \u003cp\u003eResponse Surface Methodology (RSM) modeling confirmed the quadratic relationship between coating weight, AI selection, and seedling biomass (R\u0026sup2;=0.94, P\u0026thinsp;=\u0026thinsp;0.0002) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The AI \u0026times; Coating interaction term was positive and significant (coefficient\u0026thinsp;=\u0026thinsp;0.34, P\u0026thinsp;=\u0026thinsp;0.0004), while the quadratic term for coating weight was negative (coefficient\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.12, P\u0026thinsp;=\u0026thinsp;0.0032), indicating an optimal coating threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients of the quadratic RSM model for Seedling Dry Biomass.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA: AI Selection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB: Coating Weight %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAB: Interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. R\u0026sup2; = 0.94, Adjusted R\u0026sup2; = 0.93*\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe ANOVA table (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) shows that AI selection explained 42% of variance, coating 28%, and their interaction 18%, with the remaining 12% attributable to random batch effects..\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA for the Response Surface Methodology model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9845.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1969.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e205.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA: AI Selection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1512.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1512.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e157.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB: Coating %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3515.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3515.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e366.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e846.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e846.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e595.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e520.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCor Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10441.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ns: not significant. R\u0026sup2; = 0.943, Adjusted R\u0026sup2; = 0.938.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe shift from reactive to proactive agriculture requires intervention at the seed level. Our results demonstrate that merging artificial intelligence with nano-biotechnology creates a closed-loop system that significantly improves seed resilience under abiotic stress. This approach achieves more than incremental improvements; it represents a paradigm shift in establishment efficiency. The strong synergy observed between AI-based selection and nano-bio-stimulant coating indicates that digital diagnostics and biological enhancement are mutually reinforcing components of a unified strategy.\u003c/p\u003e \u003cp\u003eThe central challenge in seed enhancement is the inherent variability within commercial seed lots. Applying advanced coatings to low-vigor seeds wastes biological resources and yields inconsistent results. Our AI-driven diagnostic platform addresses this fundamental issue by predicting physiological potential non-destructively before any treatment application. The hybrid Vision Transformer-ResNet152 model achieved exceptional accuracy (AUC-ROC\u0026thinsp;=\u0026thinsp;0.993), rivaling or exceeding performance reported in recent seed phenotyping studies using deep learning approaches. The identification of 750\u0026ndash;780 nm NIR wavelengths as key predictive features, validated through SHAP analysis, provides biological interpretability often lacking in black-box machine learning models. This spectral window corresponds to O-H stretching vibrations associated with water content and carbohydrate structure, both critical indicators of seed vigor.\u003c/p\u003e \u003cp\u003eOur hypothesis that coating efficacy depends on seed intrinsic quality was strongly supported by the experimental results. The AI-Selected\u0026thinsp;+\u0026thinsp;Coated group outperformed all controls, but notably, the Random\u0026thinsp;+\u0026thinsp;Coated group showed only marginal improvement over untreated seeds. This finding resolves a persistent paradox in the bio-stimulant literature: why do these products show high variability in field trials? Our data suggest that seed quality heterogeneity is a major confounding factor that has been systematically overlooked. When high-quality seeds receive targeted enhancement, the response is multiplicative rather than additive.\u003c/p\u003e \u003cp\u003eThe molecular mechanisms underlying this synergy were investigated through biochemical assays. The coordinated increase in proline content and antioxidant enzyme activities (SOD, CAT, APX) indicates that the nano-bio-coating induces a primed physiological state. This priming effect was most pronounced in AI-selected seeds, confirming that biochemical readiness precedes and enables phenotypic resilience. The threefold increase in proline content and 62% reduction in oxidative damage markers provide biochemical validation of the molecular data.\u003c/p\u003e \u003cp\u003eFrom an agronomic perspective, the Response Surface Methodology results offer practical guidance for commercial application. The significant negative quadratic term for coating weight indicates that more is not always better; beyond an optimal threshold, coating thickness may impede gas exchange and reduce effectiveness. This finding has direct implications for coating equipment calibration and quality control in seed treatment facilities.\u003c/p\u003e \u003cp\u003eOur layered coating design addresses multiple stress pathways simultaneously. The chitosan nanoparticle matrix ensures sustained release of L-amino acids and ascorbic acid during the critical germination window, while the synthetic microbial community establishes early rhizosphere colonization. The combination of \u003cem\u003ePseudomonas fluorescens\u003c/em\u003e and \u003cem\u003eBacillus subtilis\u003c/em\u003e was selected for complementary functions: alginate production for osmotic protection and surfactin for induced systemic resistance. This multi-mechanism approach is essential because abiotic stress operates through interconnected physiological networks that cannot be addressed by single-target interventions.\u003c/p\u003e \u003cp\u003eThe policy implications of this work extend beyond the laboratory. Current agricultural subsidy structures often reward input volume rather than efficiency, creating disincentives for precision approaches. Our data support a transition toward performance-based incentives that verify stand establishment rates and input-use efficiency. Similarly, regulatory frameworks for seed certification must evolve to accommodate non-destructive AI-based vigor testing alongside traditional germination assays. The \"right seed, right treatment\" paradigm we propose aligns with broader sustainability goals: maximizing returns on water, fertilizer, and land investments while minimizing environmental externalities.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. This study was conducted under controlled phytotron conditions that, while carefully designed to simulate field-relevant stress dynamics, cannot fully capture the complexity of natural environments. Validation across multiple field seasons, soil types, and crop species is essential before widespread adoption. Economic analysis of cost-benefit ratios for smallholder versus large-scale farming operations remains to be conducted. Additionally, integration with other precision agriculture technologies-such as variable-rate planting equipment and sensor-guided irrigation systems-could further enhance the scalability of this approach.\u003c/p\u003e \u003cp\u003eIn conclusion, this study establishes that the integration of AI-based phenotyping with nano-bio-stimulant seed coatings creates synergistic improvements in climate resilience that exceed the capabilities of either technology in isolation. By ensuring that biological inputs are matched to seeds with the physiological capacity to respond, this prescriptive approach maximizes resource efficiency and establishes a foundation for sustainable intensification of agriculture.\u003c/p\u003e"},{"header":"6. Conclusions and Forging a Policy Path for Intelligent Seed Systems","content":"\u003cp\u003eThis research goes beyond traditional seed science by creating a closed-loop, intelligent system that combines predictive digital technology with proactive biological improvement. We have shown that merging AI-based vigor diagnostics and Nano-bio-stimulant coatings is not just additive; it is multiplicative. This creates a customized approach to crop establishment that is more resilient, efficient, and scalable. Our findings, validated from molecular transcriptomes to whole-plant phenotypes under stress, offer a solid plan for rethinking our food and energy supply chains amid climate uncertainty. The impact of this work reaches far beyond the lab, requiring a united effort to turn this proof-of-concept into a global action framework. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur investigation provides three undeniable conclusions that challenge the norm in seed technology: \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. Diagnostic Precision is Key to Resilience: which in our trials with local Iranian seeds showed high accuracy of our explainable AI model (AUC=0.993) shifts seed quality assessment from a destructive, lagging indicator to a non-destructive, leading predictor of phenotypic potential. By analyzing hyperspectral signatures related to cellular integrity and metabolic capacity, this technology enables the targeted identification of seeds that can convert biological inputs into agricultural success, maximizing the return on investment for each input used. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. Synergy Drives Efficacy: The significant performance difference between the AI-Selected + Coated group and all other treatment groups under severe abiotic stress shows that the effectiveness of advanced biologicals closely depends on the seed\u0026apos;s intrinsic quality. The strong interaction effect (P=0.0003) confirms that precision selection and precision enhancement are two halves of the same whole; one without the other leads to poor returns. This resolves a long-standing inconsistency in the bio-stimulant sector and sets a new standard for efficacy trials. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Priming is a Measurable Physiological State: Our comprehensive biochemical analysis offered clear proof that the Nano-bio-coating actively creates a state of physiological readiness. The coordinated increase in proline content, antioxidant enzyme activities, and stress metabolites shows that the technology goes beyond passive protection to induce a strong, prepared state, allowing seeds to respond faster and more effectively to stress. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClosing the gap between this technological improvement and its effects on farms requires a thoughtful and coordinated policy strategy. We propose a four-pillar framework to speed up the adoption of intelligent seed systems: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. Creating Markets for Value-Added Inputs: \u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003ePerformance-Based Incentives: Change agricultural subsidy programs from supporting bulk inputs to rewarding verified results, such as stand establishment rates and input-use efficiency. Governments could pilot \u0026quot;Resilience Premium\u0026quot; vouchers for farmers who use certified climate-resilient seeds, reducing the risk of adoption and encouraging market demand. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTiered Certification Systems: Develop an international certification framework, similar to organic or non-GMO labels, for \u0026quot;AI-Optimized\u0026quot; or \u0026quot;targetedly Enhanced\u0026quot; seeds. This creates clear market differentiation and allows seed companies to realize the value of their innovation. \u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e2. Modernizing Regulatory and Seed Certification Frameworks: \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003ePathways for Integrated Products: Regulatory agencies (e.g., FAO, OECD Seed Schemes) should create new pathways for approving combined digital and biological products. This involves introducing a new category that recognizes the AI component as a vital, validated part of the improvement process, rather than just a supplementary tool. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAcceptance of Non-Destructive Testing: Push for the inclusion of validated, AI-driven vigor diagnostics as an accepted method in official seed testing protocols, moving away from the outdated 20th-century standard of slow, destructive germination tests that are less predictive of field performance. \u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e3. Fostering Inclusive Innovation and Infrastructure: \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFor instance, forming public-private-academic consortia, similar to collaborations we\u0026apos;ve seen in Iran between universities and seed companies, could develop open-source AI models for local crops. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eA hub-and-spoke production model, where central facilities equipped with AI tech process seeds for smaller companies, could lower barriers to entry- drawing from similar setups in European precision farming. \u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e4. Prioritizing Strategic Research and Development: \u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFocus on Scalability and Economics: Direct public R\u0026amp;D funding to tackle key scaling challenges: lowering the cost of Nano-carriers, improving coating methods for high-throughput seed treatment facilities, and conducting full life-cycle analysis (LCA) to measure the environmental and economic benefits of customized enhancement. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSocial Science Integration: Fund interdisciplinary research that looks into the socio-economic barriers to adoption, develops effective training programs for extension services, and models the broader economic impact of widespread use on national food and energy security. \u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe integrated system presented here is more than a technological advancement; it represents a shift toward a more rational, efficient, and resilient agricultural system. It embodies the principle of \u0026quot;more with less\u0026quot;- more yield stability with less water, more nutrient efficiency with less fertilizer, and more farmer confidence with less risk. By ensuring that the very foundation of crop production- the seed- is intelligent and resilient, we secure the first critical link in the food and energy security chain. The journey from a single fortified seed to a food-secure future is complex, but it begins with the intentional, policy-supported adoption of these prescriptive, data-driven strategies. This research provides not only the scientific evidence but also the policy roadmap needed for this essential transition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e7. Author Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMajid Ghanbari: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing \u0026ndash; original draft, Visualization.\u003c/p\u003e\n\u003cp\u003eMahdi Nasrabadi: Methodology, Validation, Investigation, Resources, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Ethics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved plant materials (\u003cstrong\u003e\u003cem\u003eTriticum aestivum\u003c/em\u003e L. cv. Pishtaz\u003c/strong\u003e seeds) and microbial strains (Pseudomonas fluorescence SRB-1 and Bacillus subtilis PWN-12) obtained from certified commercial sources. All experimental procedures complied with institutional biosafety guidelines and the Convention on Biological Diversity (CBD) regulations. The use of genetically unmodified plant seeds and non-pathogenic plant growth-promoting rhizobacteria (PGPR) did not require specific ethical approval under national regulations. Research activities were conducted in accordance with the Nagoya Protocol on Access and Benefit Sharing. No human subjects or vertebrate animals were involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. Data Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Source data for all figures and statistical analyses are provided with this submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10. Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial or non-financial interests. All authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAntle, J. M., Jones, J. W. \u0026amp; Rosenzweig, C. E. Next generation agricultural system data, models and knowledge products: Introduction. \u003cem\u003eAgric. Syst.\u003c/em\u003e \u003cb\u003e155\u003c/b\u003e, 186\u0026ndash;190 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell, B. M. et al. Agriculture production as a major driver of the Earth system exceeding planetary boundaries. \u003cem\u003eEcol. 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Applications of nanotechnology in plant growth and crop protection: A review. \u003cem\u003eMolecules\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 2558. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/molecules24142558\u003c/span\u003e\u003cspan address=\"10.3390/molecules24142558\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital Phenotyping, Microbial SynCom, Defense Priming, Precision Coating, Resource Use Efficiency","lastPublishedDoi":"10.21203/rs.3.rs-9266171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9266171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change intensifies abiotic stresses such as drought and salinity during seed germination, threatening global food security. While nano-bio-stimulant coatings and artificial intelligence for vigor diagnosis have emerged as promising tools, their integration into a smart, closed-loop system remains unexplored. Here, we present a seed enhancement platform combining AI-based predictive phenotyping with nano-bio-stimulant technology. A hybrid Vision Transformer-Deep learning model trained on hyperspectral images (400\u0026ndash;1000 nm) of 16,000 seeds achieved an AUC of 0.993 for non-destructive vigor prediction. High-vigor seeds were coated with a multi-layer formulation: a synthetic microbial community (SynCom) of Pseudomonas fluorescens and Bacillus subtilis, overlaid with chitosan nanoparticles infused with L-amino acids and ascorbic acid. Under drought stress, the AI-Selected\u0026thinsp;+\u0026thinsp;Coated group achieved 95% germination and 58% increase in seedling biomass (P\u0026thinsp;=\u0026thinsp;0.0003), significantly outperforming controls. Biochemical assays confirmed enhanced antioxidant enzyme activity and osmolyte accumulation, indicating priming of stress-responsive pathways. This study demonstrates that merging digital intelligence and nano-biotechnology creates a synergistic, scalable solution for climate-resilient agriculture.\u003c/p\u003e","manuscriptTitle":"An integrated artificial intelligence and nano-bio-stimulant seed coating system enhances climate resilience through predictive phenotyping","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 09:09:30","doi":"10.21203/rs.3.rs-9266171/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce06848f-09f5-44cb-9f7f-870db080eda6","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-13T10:01:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T03:36:01+00:00","index":72,"fulltext":""},{"type":"reviewerAgreed","content":"152340947912852241287933284220650080523","date":"2026-05-08T07:17:59+00:00","index":71,"fulltext":""},{"type":"reviewerAgreed","content":"72744641404001390110645406105024948090","date":"2026-05-06T04:22:36+00:00","index":69,"fulltext":""},{"type":"reviewerAgreed","content":"120840765643132798427396509584302794966","date":"2026-05-06T04:16:52+00:00","index":68,"fulltext":""},{"type":"reviewerAgreed","content":"249521139390063709616517809058747625252","date":"2026-05-06T03:28:57+00:00","index":67,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T00:54:42+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"234382348346660938629946651935843084576","date":"2026-05-05T23:33:17+00:00","index":65,"fulltext":""},{"type":"reviewersInvited","content":"11","date":"2026-05-05T21:11:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T21:10:37+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67763096,"name":"Biological sciences/Biotechnology"},{"id":67763098,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-05-13T10:15:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 09:09:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9266171","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9266171","identity":"rs-9266171","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}