Advances in Machine Learning for High-Throughput Plant Phenotyping: Techniques, Applications, and Opportunities

Abstract

High-throughput plant phenotyping (HTP) has emerged as a crucial element of modern plant science and crop breeding, enabling the swift, large-scale, and non-invasive evaluation of plant characteristics. The integration of machine learning (ML), particularly deep learning approaches, has transformed HTP by automating feature extraction, improving predictive accuracy, and enabling thorough analysis under diverse environmental conditions. This review compiles the latest advancements in ML-based phenotyping, covering traditional algorithms, convolutional neural networks, transformer models, and innovative 3D reconstruction techniques. It investigates advanced phenotyping technologies, encompassing controlledenvironment systems, field robotics, and UAV-driven imaging, along with novel instruments such as ChronoRoot 2.0 and PhenoAssistant. The conversation includes uses in yield forecasting, stress identification, trait measurement, and weed differentiation. Additionally, it examines significant issues like dataset constraints, interpretability of models, and scalability, while suggesting future paths that involve multimodal integration, open data standards, explainable AI, and affordable phenotyping methods. This synthesis is designed to help researchers leverage ML for phenotyping processes, thereby promoting precision agriculture and accelerating breeding initiatives

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