Call for papers: Phenomic prediction in plant breeding

High-throughput phenotyping (HTP) and predictive modeling are transforming plant breeding. The Plant Phenome Journal is putting together a special section that will highlight advances in combining sensor-derived data (RGB, multispectral, LiDAR) with machine and deep learning for trait prediction in diverse crops. The journal is calling for submissions to the special section through Nov. 1, 2027. In particular, it is inviting papers on direct applications indicating the potential implementation of phenomics as a predictive tool to enhance selection accuracy, selection intensity, elite germplasm characterization, or gains in temporal, spatial, or resource efficiency in breeding programs.

Topics for this call for papers include but are not restricted to:

• Enhancing selection accuracy and/or selection intensity
• Advances in combining sensor-derived data (RGB, multispectral, and LiDAR) with machine and deep learning for trait prediction
• Elite germplasm characterization or gains in temporal, spatial or resource efficiency in breeding programs

Learn more.