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

TL;DR

This paper reviews how artificial intelligence can help detect non-living stress factors in plants, offering a more efficient and accurate solution for agriculture.

Contribution

The paper provides a comprehensive review of AI applications in abiotic stress detection, contrasting with other reviews that focus on individual technologies.

Findings

AI, especially machine and deep learning, offers non-invasive and sustainable abiotic stress detection in plants.

AI-based methods show potential for higher accuracy and scalability compared to traditional approaches.

The paper identifies challenges in adopting AI for abiotic stress detection in agriculture.

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.

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