Artificial Intelligence (AI) in Detection of Abiotic Stress in Plants: A Review.

Abstract

Global agriculture is facing significant threat from climate-driven abiotic stress, which endangers global food security by impacting crop performance and adaptation. However, traditional abiotic stress detection methods are often labor-intensive and lack precision and scalability. Efficient and reliable solutions are needed to meet rising global food demand. Recent advances in artificial intelligence (AI) offer highly accurate, non-invasive, and sustainable approaches for abiotic stress detection. This paper reviews the impact of AI, and specifically Machine and Deep Learning algorithms, coupled with synergistic technologies and diverse datasets (imaging techniques and Internet of Things (IoT) infrastructures), to identify unique signatures of abiotic stress, and assess its impact on growth and physiological performance. It contrasts with other reviews that address individual technologies and algorithms, while presenting abiotic stress detection as a secondary objective. We examined peer-reviewed journal articles on the use of AI in detecting abiotic stress. The reviewed literature was chosen based on the stress category, sensing mode, and AI technologies employed. A comparative analysis was performed to explore potential advancements of AI-based abiotic stress detection methods over traditional approaches and also challenges lied to the adoption of AI in agriculture for abiotic stress detection.

Plant phenotyping relevance

植物の非生物的ストレス状態をAI・画像・IoT等で検出する手法を主題としたレビューであり、植物フェノタイピング手法のレビューに該当する。

Code and data availability

This is a review article on AI for abiotic stress detection with no original phenotyping measurements, datasets, images, or analysis code. The Data Availability Statement says 'Not applicable.' The only URLs in the allowed list (WEF article, Microsoft FarmBeats) are cited background references, not paper-specific repro

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