Decision-Making Approach for Early Plant Stress Detection from Hyperspectral Images

Smart agriculture is based on advanced technologies such as artificial intelligence to enhance agricultural efficiency while minimizing the use of resources. Collecting multimodal data provides inputs for both predictive models and decision support. In this context, hyperspectral (HS) images aim to support crop monitoring, facilitate disease detection processes, and tackle water scarcity challenges by promptly identifying water stress. The domain of computer vision-assisted smart agriculture is continually progressing, marked by a substantial volume of recent scientific literature detailing advancements in this domain. This progression aligns with the numerous advancements achieved in the realm of deep learning over the past few years. The objective of this paper is to introduce a methodology for constructing a model that classifies the level of water stress using a dataset comprised of image series of water-stressed plants, eliminating the necessity for additional precise labeling. Once constructed, this model requires only a single hyperspectral image of a plant to ascertain the degree of water stress.