Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stress Identification

arXiv:2404.10073v2 Announce Type: replace
Abstract: Early identification of drought stress in crops is vital for implementing effective mitigation measures and reducing yield loss. Non-invasive imaging techniques hold immense potential by capturing subtle physiological changes in plants under water deficit. Sensor based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis aimed at identifying drought stress. While these approaches yield favorable results, real-time field applications requires algorithms specifically designed for the …

abstract arxiv crops cs.cv data deep learning deficit drought identification imaging information light loss pipeline plants sensor stress type vital water