Deep learning based abiotic crop stress assessment for precision agriculture: A comprehensive review

Abiotic stresses are a leading cause of crop loss and a severe peril to global food security. Precise and prompt identification of abiotic stresses in crops is crucial for effective mitigation strategies. In recent years, Deep learning (DL) techniques have demonstrated remarkable promise for high-throughput crop stress phenotyping using remote sensing and field data. This study offers a comprehensive review of the applications of DL models like artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), vision transformers (ViT), and other advanced deep learning architectures for abiotic crop stress assessment using different modalities like IoT sensor data, thermal, spectral, RGB with field, UAV and satellite based imagery. The study comprehensively analyses the abiotic stress conditions due to (a) water (b) nutrients (c) salinity (d) temperature and (e) heavy metal. Key contributions in the literature on stress classification, localization, and quantification using deep learning approaches are discussed in detail. The study also covers the principles of deep learning models, and their unique capabilities for handling complex, high-dimensional datasets inherent in abiotic crop stress assessment. The review also highlights important challenges and future directions in deep learning based abiotic crop stress assessment like limited labelled data, model interpretability, and interoperability for robust stress phenotyping. This study critically examines the research pertaining to the abiotic crop stress assessment, and provides a comprehensive view of the role deep learning plays in advancing abiotic crop stress assessment for data-driven precision agriculture.