High-Throughput Phenotyping and Machine Learning for Plant Breeding

Dear Colleagues,

High-throughput phenotyping (HTP) of plants in field and controlled conditions has the potential to accelerate selection of genotypes with improved trait characteristics for plant breeding. Unmanned aerial vehicles, ground vehicles and hand-held sensors have shown tremendous potential for field phenotyping.High-throughput phenotyping facilities in controlled conditions can further accelerate germplasm characterization. Challenges remain in acquisition of the phenotypic data, data management, curation, analysis and sharing. This special issue on High-Throughput Phenotyping and Machine Learning for Plant Breeding aims to compile articles demonstrating the implementation of HTP in plant breeding. Both research and review articles are welcome.

Potential topics for the special issue:

•  Implementation of HTP to improve genetic gain
•  New advances in HTP methods
•  Estimation of cost-efficiency of HTP
•  New HTP methods for affordable phenotyping
•  Deep learning approaches to identify and quantify key traits
•  Application of machine learning for data integration
•  New open source software for analysis of phenotypic data
•  New databases for management and sharing of phenotypic data

Associate Professor Aakash Chawade

Guest Editor Associate Professor Aakash Chawade Department of Plant Breeding, Swedish University of Agricultural Sciences, SE-23053 Alnarp, Sweden