Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery
Xuqi Lu, Yutao Shen, Jiayang Xie, Xin Yang, Shu Qingyao, Song Chen, Z. T. Shen, Haiyan Cen
Abstract
) of 0.73 and a root mean square error (RMSE) of 28.3, and the overall panicle classification accuracy reached 94.8%. The proposed approach enhances operational efficiency and automates the process from plot cropping to PNpA prediction, which is promising for accelerating the selection of desired traits in rice breeding.
Topics & Concepts
PanicleArtificial intelligenceComputer scienceSegmentationPattern recognition (psychology)Computer visionAgronomyBiologySmart Agriculture and AIRemote Sensing in AgricultureSpectroscopy and Chemometric Analyses