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

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.

This work presents an integrated multispectral phenotyping framework 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. 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. This work provides a replicable model for interdisciplinary collaboration in plant phenotyping research.

Status: Under revision to the Plant Phenome Journal

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Recommended citation: Fahimeh Orvati Nia, Joshua Peeples, Seth C Murray, Andrew McFarland, and colleagues. (2026). "A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments." bioRxiv. 2026.02.25.707797.
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