Recent Methods for Evaluating Crop Water Stress Using AI Techniques: A Review.

Abstract

This study systematically reviews the integration of artificial intelligence (AI) and remote sensing technologies to address the issue of crop water stress caused by rising global temperatures and climate change; in particular, it evaluates the effectiveness of various non-destructive remote sensing platforms (RGB, thermal imaging, and hyperspectral imaging) and AI techniques (machine learning, deep learning, ensemble methods, GAN, and XAI) in monitoring and predicting crop water stress. The analysis focuses on variability in precipitation due to climate change and explores how these technologies can be strategically combined under data-limited conditions to enhance agricultural productivity. Furthermore, this study is expected to contribute to improving sustainable agricultural practices and mitigating the negative impacts of climate change on crop yield and quality.

Code and data availability

This is a review article summarizing prior literature on AI-based crop water stress assessment. The supplied blocks contain no public phenotype datasets, plant/sensor images, author analysis code, trained models, or supplements with such assets; only literature search methodology and summary tables of cited studies are

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