A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments

Abstract Advances in automation, imaging, and artificial intelligence have enabled researchers to capture large volumes of high-quality plant data for understanding crop growth, stress, and genotype-by-environment interactions. While genomics has achieved remarkable throughput, phenotypic data acquisition remains a critical bottleneck for accelerating crop improvement and biological discovery. To address this challenge, an integrated multispectral phenotyping framework was developed using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse, a fully controlled facility designed for reproducible plant monitoring throughout the entire growth cycle of most crops. The framework expands the Plant Growth and Phenotyping (PGP v2) dataset and establishes a standardized system for continuous image acquisition, segmentation, deep feature extraction, and temporal analysis across multiple crop species. The project was organized around five coordinated areas: Administration and Coordination, Imaging and Sensor Operations, Data Processing and Management, Artificial Intelligence and Analytics, and Plant Science and Discovery. This structure ensured consistent data quality, version-controlled workflows, and communication across disciplines. The analytical pipeline integrates pseudo-RGB generation, deep learning–based detection and segmentation, image stitching, and temporal (longitudinal) tracking to isolate individual plants and analyze changes in morphology, spectral reflectance, and texture over time. Beyond technical innovation, the framework provides a replicable model for interdisciplinary collaboration and administrative integration in plant phenomics. The combined dataset, workflow, and management framework enable scalable, reproducible, and data-driven plant science research that bridges engineering and biological discovery. Plain Language Summary Temporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research. Competing Interest Statement The authors have declared no competing interest. - Abbreviations - AI - Artificial Intelligence - ARI - Anthocyanin Reflectance Index - BEN - Background Erase Net-work - BiRefNet - Bilateral Reference Network - CNN - Convolutional Neural Network - EHD - Edge Histogram Descriptor - GNDVI - Green Normalized Difference Vegetation Index - HOG - Histogram of Oriented Gradients - L - Lacunarity - LBP - Local Binary Pattern - ML - Machine Learning - MSIS - Multispectral Imaging System - NIR - Near-Infrared (spectral band) - NDVI - Normalized Difference Vegetation Index - PGP - Plant Growth and Phenotyping - RE - Red-Edge (spectral band) - RGB - Red-Green-Blue composite image - ROI - Region of Interest - SAM - Segment Anything Model - SAM2Long - Segment Anything Model for Long Sequences - SDK - Soft-ware Development Kit - YOLO - You Only Look Once (object detection model) - UAV - Unmanned Aerial Vehicle - BRISK - Binary Robust Invariant Scalable Keypoints - ORB - Oriented FAST and Rotated BRIEF - SIFT - Scale-Invariant Feature Transform - AKAZE - Accelerated KAZE;