title: Machine Learning for High-Throughput Stress Phenotyping in Plants.
abstract: Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.
authors.affiliation: Department of Agronomy, Iowa State University, Ames, IA, USA. Electronic address: arti@iastate.edu.
authors.forename: Arti
authors.initials: A
authors.lastname: Singh
authors.affiliation: Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.
authors.forename: Baskar
authors.initials: B
authors.lastname: Ganapathysubramanian
authors.affiliation: Department of Agronomy, Iowa State University, Ames, IA, USA.
authors.forename: Asheesh Kumar
authors.initials: AK
authors.lastname: Singh
authors.affiliation: Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.
authors.forename: Soumik
authors.initials: S
authors.lastname: Sarkar
mesh_terms: D001947:Breeding; D057166:High-Throughput Screening Assays; D000069550:Machine Learning; D010641:Phenotype; D010944:Plants / Q000378:metabolism*; D013312:Stress, Physiological
keywords: Imaging; abiotic stress; biotic stress; high-throughput phenotyping; machine learning; plant breeding