Deep Learning in Plant Stress Phenomics Studies - A Review

Abstract

Efficient crop management and treatment rely on early detection of plant stress. Imaging sensors provide a non-destructive and commonly used method for detecting stress in large farm fields. With machine learning and image processing, several automated plant stress detection methods have been developed. This technology can analyze large sets of plant images, identifying even the most subtle spectral and morphological characteristics that indicate stress. This can help categorize plants as either stressed or not, with significant implications for farmers and agriculture managers. Deep learning has shown great potential in vision tasks, making it an ideal candidate for plant stress detection. This comprehensive review paper explores the use of deep learning for detecting biotic and abiotic plant stress using various imaging techniques. A systematic bibliometric review of the Scopus database was conducted, using keywords to shortlist and identify significant contributions in the literature. The review also presents details of public and private datasets used in plant stress detection studies. The insights gained from this study will significantly contribute to developing more profound deep-learning applications in plant stress research, leading to more sustainable crop production systems. Additionally, this study will assist researchers and botanists in developing plant types resilient to various stresses.