An integrated rice panicle phenotyping method based on X-ray and RGB scanning and deep learning
Lejun Yu, Jiawei Shi, Chenglong Huang, Lingfeng Duan, Di Wu, Debao Fu, Changyin Wu, Lizhong Xiong, Wanneng Yang, Qian Liu
Abstract
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 (R2 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.
Topics & Concepts
PanicleThreshingJaponica riceRGB color modelKernel (algebra)Artificial intelligenceBiologyMathematicsComputer scienceAgronomyPattern recognition (psychology)JaponicaBotanyCombinatoricsSmart Agriculture and AIGABA and Rice ResearchGenetic Mapping and Diversity in Plants and Animals