Hyperspectral Remote Sensing and AI for Early Crop Stress Detection
DOI:
https://doi.org/10.5281/zenodo.22042928Keywords:
Crop Stress Detection, Precision Agriculture, Vegetation Indices, Biotic and Abiotic Stress, Deep LearningAbstract
Global food security is facing a threat by biotic and abiotic stresses including drought, nutrient deficiency, pathogen infection, pest infestation, and salinity that reduce crop yield and quality. Conventional stress monitoring relies on visual scouting and destructive sampling, which are labour-intensive, subjective, and often detect stress only after irreversible physiological damage has occurred. This review discusses the role of Hyperspectral remote sensing (HSI) and Artificial Intelligence (AI) in the early crop stress detection. Unlike conventional methods HSI captured detailed spectral information that can identify changes in plant health before visible symptoms are visible. The AI techniques like Machine learning and Deep learning helps analyzing the large and complex datasets to improve the accuracy of the crop stress detection. This article reviews recent studies on the use of HSI and AI for detecting crop stress like drought, salinity, nutrient deficiency. It also discusses their role and application in precision agriculture and the challenges associated with data management, computational requirements and sensor cost. Overall, the integration of HSI and AI provides effective approach to early crop stress detection helping improve crop management and to reduce yield losses. The synergy of hyperspectral remote sensing and artificial intelligence represents a paradigm shift in precision agriculture, offering unprecedented capability for early, accurate, and scalable crop stress detection.
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Copyright (c) 2026 Aman Nath Bag, Amit Ranjan Swain, Masina Sairam, Shaik Rishitha, Sumit Ray, Sagar Maitra
This work is licensed under a Creative Commons Attribution 4.0 International License.
