PhytoNet: Mish-Optimized Deep Learning Architecture for Enhanced Tomato Leaf Disease Detection

PhytoNet: Mish-Optimized Deep Learning Architecture for Enhanced Tomato Leaf Disease Detection | 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 Research Article PhytoNet: Mish-Optimized Deep Learning Architecture for Enhanced Tomato Leaf Disease Detection Deependra Rastogi, Prashant Johri, Varun Tiwari, Anand Singh, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8970511/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 Tomato leaf diseases significantly impact agricultural productivity, necessitating accurate and efficient diagnostic methods. Deep learning has emerged as a robust approach for plant disease detection, but challenges such as inefficient feature extraction, classification, model complexity and limited computational resources hinder its widespread adoption. This study introduces a PhytoNet (Mish-Optimized SqueezeNet) Framework to enhance tomato leaf disease prediction. The SqueezeNet architecture, known for its lightweight design, is optimized with the Mish activation function to improve feature extraction and classification capabilities while maintaining computational efficiency. The methodology involves training the SqueezeNet model on 10 classes of mendaley dataset of tomato leaf images, encompassing multiple disease classes and healthy samples. Data preprocessing techniques, including image augmentation and normalization, are employed to ensure model robustness. The integration of the Mish activation function in critical layers enhances non-linearity, aiding in better gradient flow and improved performance during training. Model evaluation is conducted using metrics such as accuracy, precision, recall and F1-score. Experimental results demonstrate that the PhytoNet outperforms traditional SqueezeNet and other lightweight architectures in terms of classification accuracy, achieving over 0.9957 accuracy on the dataset. Additionally, the model maintains low computational overhead, making it suitable for deployment on resource-constrained devices. Hence, the proposed framework effectively balances accuracy and efficiency, addressing critical limitations in existing plant disease detection models. This work underscores the potential of lightweight and activation-optimized deep learning frameworks for real-time agricultural applications, paving the way for scalable and sustainable solutions in precision farming. Plant Leaf Disease Identification Deep Learning SqueezeNet Mish-Optimized Function Full Text Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8970511","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611471573,"identity":"5c864044-fc6c-4f29-a068-2c20d30e8e08","order_by":0,"name":"Deependra Rastogi","email":"","orcid":"","institution":"IILM University","correspondingAuthor":false,"prefix":"","firstName":"Deependra","middleName":"","lastName":"Rastogi","suffix":""},{"id":611471574,"identity":"6dd3cd20-a0c6-4b28-8a16-98dcd56be454","order_by":1,"name":"Prashant Johri","email":"","orcid":"","institution":"Galgotias 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Deep learning has emerged as a robust approach for plant disease detection, but challenges such as inefficient feature extraction, classification, model complexity and limited computational resources hinder its widespread adoption. This study introduces a PhytoNet (Mish-Optimized SqueezeNet) Framework to enhance tomato leaf disease prediction. The SqueezeNet architecture, known for its lightweight design, is optimized with the Mish activation function to improve feature extraction and classification capabilities while maintaining computational efficiency. The methodology involves training the SqueezeNet model on 10 classes of mendaley dataset of tomato leaf images, encompassing multiple disease classes and healthy samples. Data preprocessing techniques, including image augmentation and normalization, are employed to ensure model robustness. 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