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Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform

文献类型： 外文期刊

作者： Zhou, Chengquan 1 ; Ye, Hongbao 1 ; Hu, Jun 1 ; Shi, Xiaoyan 1 ; Hua, Shan 1 ; Yue, Jibo 2 ; Xu, Zhifu 1 ; Yang, Guijun 2 ;

作者机构： 1.ZAAS, Inst Agr Equipment, Hangzhou 310000, Zhejiang, Peoples R China

2.Minist Agr China, Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr, Beijing 100089, Peoples R China

3.Minist Agr, Key Lab Agriinformat, Beijing 100089, Peoples R China

关键词： rice panicle counting; UAV platform; deep learning; yield estimation

期刊名称：SENSORS （ 影响因子：3.576； 五年影响因子：3.735 ）

ISSN： 1424-8220

年卷期： 2019 年 19 卷 14 期

页码：

收录情况： SCI

摘要： The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were conducted in target areas to train and test the system and used a rotor light unmanned aerial vehicle equipped with a high-definition RGB camera to collect images. The trained model achieved a precision of 0.868 on a held-out test set, which demonstrates the feasibility of this approach. The algorithm can deal with the irregular edge of the rice panicle, the significantly different appearance between the different varieties and growing periods, the interference due to color overlapping between panicle and leaves, and the variations in illumination intensity and shading effects in the field. The result is more accurate and efficient recognition of rice-panicles, which facilitates rice breeding. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a global scale.

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