A Novel Tool for Soybean Pods and Branches Recognition by Deep Learning Based Approach

文献类型： 外文期刊

第一作者： Xie, Qize

作者： Xie, Qize;Yang, Qing;Liu, Zhi;Wei, Yu;Yan, Long;Xie, Qize;Tao, Peijun;Du, Hongbo;Jin, Xin;Li, Chuanfeng;Tang, Futong

作者机构：

关键词： computer vision; ConvNeXt-S; deep learning; soybean branch; soybean pod

期刊名称：PLANT BREEDING （ 影响因子：1.8； 五年影响因子：2.0 ）

ISSN： 0179-9541

年卷期： 2024 年

页码：

收录情况： SCI

摘要： Phenotypic trait identification is crucial in cultivating new soybean varieties with high yield and quality. The traditional soybean phenotypic trait identification relies on manual pod counting and plant height measuring with a ruler. The heavy workload causes the data collected by human resources to be extremely prone to error. Therefore, developing an efficient and high-quality method to obtain phenotypic data of soybean pods and branches is urgently needed. Three network models including ResNet-101, Swin-S and ConvNeXt-S are compared in this study, and the ConvNeXt-S model is identified as optimal, with a mAP@0.5 of 0.95, which could reach 74.2%. A deep learning-based approach soybean plant phenotype detection and data storage system is developed, including information on plants, pods and branches. The R2 between the number of pods detected by the system and the true value reached 0.995. These results indicate that the system is more accurate and stable than the manual phenotype identification. Our study paves the way for reducing economic and time costs as well as improving phenotype identification efficiency and accuracy in detecting soybean phenotypes.

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