Advanced Machine Learning Techniques for Real-time Monitoring, Analysis and Optimization of Legume Crop Growth

Abstract

Background: This review study examines the transformative potential of machine learning (ML) methods for real-time and continuous evaluation of legume crop development. It provides a structured and comprehensive synthesis of current ML applications, highlighting their potential to improve legume crop management in terms of accuracy, efficiency, scalability and sustainability. Unlike existing reviews, this study specifically emphasizes real-time monitoring frameworks that integrate multi-source data (satellite, UAV, IoT and sensors) for legume crops. Methods: Various machine learning methods, including supervised, unsupervised and deep learning paradigms, are reviewed with respect to their applications in crop health prediction, disease detection and yield estimation. The review further analyzes the integration of ML models with Internet of Things (IoT), edge computing and sensor-based systems to address challenges related to data quality, model interpretability, computational efficiency and real-time decision-making. Result: While challenges remain, such as data heterogeneity, limited model generalization and the integration of ML with traditional agronomic practices, recent technological advancements demonstrate promising solutions. Key trends include the development of robust and transferable models, improved human–machine interfaces and decision-support tools for farmers. These advances have the potential to enhance precision, resilience and sustainability in legume crop monitoring, thereby contributing to global food security and climate-smart agriculture.

Plant phenotyping relevance

マメ科作物の生育・健康・病害・収量を対象に、機械学習と衛星・UAV・IoT・センサーを統合したモニタリング手法をレビューしており、植物形質・状態の取得および推定方法が中心である。

Code and data availability

This is a narrative literature review of ML techniques for legume crop monitoring. It reports no original plant-phenotyping measurements, images, sensor data, analysis code, or trained models. The only data statement is generic ('will be made available from the corresponding authors upon reasonable request'), which is

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