A Review of Artificial Intelligence Techniques for Wheat Crop Monitoring and Management
Jayme Garcia Arnal Barbedo
Artificial intelligence (AI) techniques, particularly machine learning and deep learning, have shown great promise in advancing wheat crop monitoring and management. However, the application of AI in this domain faces persistent challenges that hinder its full potential. Key limitations include the high variability of agricultural environments, which complicates data acquisition and model generalization; the scarcity and limited diversity of labeled datasets; and the substantial computational demands associated with training and deploying deep learning models. Additionally, difficulties in ground-truth generation, cloud contamination in remote sensing imagery, coarse spatial resolution, and the “black-box” nature of deep learning models pose significant barriers. Although strategies such as data augmentation, semi-supervised learning, and crowdsourcing have been explored, they are often insufficient to fully overcome these obstacles. This review provides a comprehensive synthesis of recent advancements in AI for wheat applications, critically examines the major unresolved challenges, and highlights promising directions for future research aimed at bridging the gap between academic development and real-world agricultural practices.
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