Optical imaging spectroscopy coupled with machine learning for detecting heavy metal of plants: A review - Li, Junmeng; Ren, Jie; Cui, Ruiyan; Yu, Keqiang; Zhao, Yanru (Frontiers in Plant Science, 2022)
Heavy metals destroy the internal structure of plants and affect the growth, development, and metabolism of plants, even threatening the health of humans and animals by the food chain. Excessive heavy metals affect the phenotypic information changes of plants, resulting in root branching, root tip number reduction, leaves wilting, and seed germination rate reduction. Therefore, finding advanced methods to characterize the phenotypic information of plants under heavy metals stress is a significant problem for precision agriculture management and breeding. As an efficient, accurate, sensitive, and reliable method, spectral analysis has broad application in plant growth monitoring, pest and disease detection, and heavy metal phenotype detection. This paper briefly introduces the principles and characteristics of some spectral techniques and their applications in detecting heavy metals in the roots, stems, leaves, and fruits of plants. Moreover, this review critically discusses the advantages and limitations of these spectral techniques and the prospects of their application in heavy metal stress detection.