数字农科院2.0

Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model

文献类型： 外文期刊

作者： Boteng Sun;Wei Zhou1; Shilin Zhu; Song Huang;Xun Yu;Zhenyuan Wu;Xiaolong Lei ;Dameng Yin; Haixiao Xia;Yong Chen;Fei Deng;Youfeng Tao; Hong Cheng; Xiuliang Jin;Wanjun Ren

期刊名称： Frontiers in Plant Science

ISSN： 1664-462X

年卷期： 2022 年

页码：

收录情况： SCIE（2022版）

摘要： Accurate and rapid identifification of the effective number of panicles per unit area is crucial for the assessment of rice yield. As part of agricultural development, manual observation of effective panicles in the paddy fifield is being replaced by unmanned aerial vehicle (UAV) imaging combined with target detection modeling. However, UAV images of panicles of curved hybrid Indica rice in complex fifield environments are characterized by overlapping, blocking, and dense distribution, imposing challenges on rice panicle detection models. This paper proposes a universal curved panicle detection method by combining UAV images of different types of hybrid Indica rice panicles (leaf-above-spike, spike-above-leaf, and middle type) from four ecological sites using an improved You Only Look Once version 4 (YOLOv4) model. MobileNetv2 is used as the backbone feature extraction network based on a lightweight model in addition to a focal loss and convolutional block attention module for improved detection of curved rice panicles of different varieties. Moreover, soft non-maximum suppression is used to address rice panicle occlusion in the dataset. This model yields a single image detection rate of 44.46 FPS, and mean average precision, recall, and F1 values of 90.32%, 82.36%, and 0.89%, respectively. This represents an increase of 6.2%, 0.12%, and 16.24% from those of the original YOLOv4 model, respectively. The model exhibits superior performance in identifying different strain types in mixed and independent datasets, indicating its feasibility as ageneral model for detection of different types of rice panicles in the heading stage

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