Application of computer vision in assessing crop abiotic stress: A systematic review

Abstract

Background Abiotic stressors impair crop yields and growth potential. Despite recent developments, no comprehensive literature review on crop abiotic stress assessment employing deep learning exists. Unlike conventional approaches, deep learning-based computer vision techniques can be employed in farming to offer a non-evasive and practical alternative. Methods We conducted a systematic review using the revised Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to assemble the articles on the specified topic. We confined our scope to deep learning-related journal articles that focused on classifying crop abiotic stresses. To understand the current state, we evaluated articles published in the preceding ten years, beginning in 2012 and ending on December 18, 2022. Results After the screening, risk of bias, and certainty assessment using the PRISMA checklist, our systematic search yielded 14 publications. We presented the selected papers through in-depth discussion and analysis, highlighting current trends. Conclusion Even though research on the domain is scarce, we encountered 11 abiotic stressors across 7 crops. Pre-trained networks dominate the field, yet many architectures remain unexplored. We found several research gaps that future efforts may fill.

Plant phenotyping relevance

作物の非生物的ストレスをコンピュータビジョンで分類・評価する研究を体系的にレビューしており、植物状態の画像ベース推定が中心です。

Code and data availability

This is a systematic review of prior literature; it reports no original plant-phenotyping measurements, images, models, or analysis code. The supporting information files (S1–S8) contain only review-process artifacts (PRISMA checklist, search strings, extraction/assessment sheets), not phenotype datasets or author phen

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