An integrated rice panicle phenotyping method based on X-ray and RGB scanning and deep learning

Rice panicle phenotyping is required in rice breeding for high yield and grain quality. To fully evaluate spikelet and kernel traits without threshing and hulling, using X-ray and RGB scanning, we developed an integrated rice panicle phenotyping system and a corresponding image analysis pipeline. We compared five methods of counting spikelets and found that Faster R-CNN achieved high accuracy (R-2 of 0.99) and speed. Faster R-CNN was also applied to indica and japonica classification and achieved 91% accuracy. The proposed integrated panicle phenotyping method offers benefit for rice functional genetics and breeding. (C) 2020 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.

Automated summary

Rice panicle phenotyping is crucial in rice breeding, allowing for the evaluation of spikelet and kernel traits using X-ray and RGB scanning to improve accuracy and speed of analysis, benefiting rice breeding efforts.