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

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2024 Oct 16

PMID 39409355

Authors

Soo Been Cho

Hidayat Mohamad Soleh

Ji Won Choi

Woon-Ha Hwang

Hoonsoo Lee

Young-Son Cho

Byoung-Kwan Cho

Moon S Kim

Insuck Baek

Geonwoo Kim

Affiliations

Soon will be listed here.

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.

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