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

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.

Copyright: © 2023 Orka et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.