Zhu Rongsheng, Wang Xueying, Yan Zhuangzhuang, Qiao Yinglin, Tian Huilin, Hu Zhenbang, Zhang Zhanguo, Li Yang, Zhao Hongjie, Xin Dawei, Chen Qingshan
College of Arts and Sciences, Northeast Agricultural University, Harbin, China.
College of Engineering, Northeast Agricultural University, Harbin, China.
Front Plant Sci. 2022 Jul 13;13:922030. doi: 10.3389/fpls.2022.922030. eCollection 2022.
The soybean flower and the pod drop are important factors in soybean yield, and the use of computer vision techniques to obtain the phenotypes of flowers and pods in bulk, as well as in a quick and accurate manner, is a key aspect of the study of the soybean flower and pod drop rate (PDR). This paper compared a variety of deep learning algorithms for identifying and counting soybean flowers and pods, and found that the Faster R-CNN model had the best performance. Furthermore, the Faster R-CNN model was further improved and optimized based on the characteristics of soybean flowers and pods. The accuracy of the final model for identifying flowers and pods was increased to 94.36 and 91%, respectively. Afterward, a fusion model for soybean flower and pod recognition and counting was proposed based on the Faster R-CNN model, where the coefficient of determination between counts of soybean flowers and pods by the fusion model and manual counts reached 0.965 and 0.98, respectively. The above results show that the fusion model is a robust recognition and counting algorithm that can reduce labor intensity and improve efficiency. Its application will greatly facilitate the study of the variable patterns of soybean flowers and pods during the reproductive period. Finally, based on the fusion model, we explored the variable patterns of soybean flowers and pods during the reproductive period, the spatial distribution patterns of soybean flowers and pods, and soybean flower and pod drop patterns.
大豆花荚脱落是影响大豆产量的重要因素，利用计算机视觉技术快速、准确地批量获取花荚表型，是研究大豆花荚脱落率的关键环节。本文比较了多种用于识别和计数大豆花荚的深度学习算法，发现Faster R-CNN模型性能最佳。此外，基于大豆花荚的特征对Faster R-CNN模型进行了进一步改进和优化。最终模型识别花荚的准确率分别提高到了94.36%和91%。之后，基于Faster R-CNN模型提出了一种大豆花荚识别与计数融合模型，该融合模型对大豆花荚计数与人工计数之间的决定系数分别达到了0.965和0.98。上述结果表明，该融合模型是一种可靠的识别与计数算法，能够降低劳动强度、提高效率。其应用将极大地促进对大豆生殖期花荚变化规律的研究。最后，基于该融合模型，探究了大豆生殖期花荚的变化规律、花荚的空间分布格局以及花荚脱落规律。
