Abiotic and biotic stresses cost agriculture in excess of $200 billion and jeopardise food security. With a rapid development of various omics platforms, plant phenotyping has become a major hurdle in breeding programs. Currently, the spatial and temporal data are collected using autonomous, semi-autonomous, and manual platforms outfitted with one or more sensors, producing enormous volumes of data for storage and analysis. Machine learning offers a unique opportunity to speed up this process, paving a pathway for better, quicker, and more efficient data handling. This Collection of Functional Plant Biology explores cutting-edge phenotyping applications of machine learning approaches for stress detection and plant phenotyping at the different scales of their structural organisation.

Collection Editors
Jungpil Shin (University of Aizu, Japan)
Md. Al Mehedi Hasan (Rajshahi University of Engineering and Technology, Bangladesh)
Yong Seok Hwang (Kwangwoon University, Korea)