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Automatic Counting of in situ Rice Seedlings from UAV Images Based on a Deep Fully Convolutional Neural Network

文献类型： 外文期刊

作者： Wu, Jintao 1 ; Yang, Guijun 1 ; Yang, Xiaodong 1 ; Xu, Bo 2 ; Han, Liang 1 ; Zhu, Yaohui 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Minist Agr, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Anhui, Peoples R China

关键词： rice seedlings; object counting; computer vision; deep learning; fully convolutional neural networks

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2019 年 11 卷 6 期

页码：

收录情况： SCI

摘要： The number of rice seedlings in the field is one of the main agronomic components for determining rice yield. This counting task, however, is still mainly performed using human vision rather than computer vision and is thus cumbersome and time-consuming. A fast and accurate alternative method of acquiring such data may contribute to monitoring the efficiency of crop management practices, to earlier estimations of rice yield, and as a phenotyping trait in breeding programs. In this paper, we propose an efficient method that uses computer vision to accurately count rice seedlings in a digital image. First, an unmanned aerial vehicle (UAV) equipped with red-green-blue (RGB) cameras was used to acquire field images at the seedling stage. Next, we use a regression network (Basic Network) inspired by a deep fully convolutional neural network to regress the density map and estimate the number of rice seedlings for a given UAV image. Finally, an improved version of the Basic Network, the Combined Network, is also proposed to further improve counting accuracy. To explore the efficacy of the proposed method, a novel rice seedling counting (RSC) dataset was built, which consisted of 40 images (where the number of seedlings varied between 3732 and 16,173) and corresponding manually-dotted annotations. The results demonstrated high average accuracy (higher than 93%) between counts according to the proposed method and manual (UAV image-based) rice seedling counts, and very good performance, with a high coefficient of determination (R-2) (around 0.94). In conclusion, the results indicate that the proposed method is an efficient alternative for large-scale counting of rice seedlings, and offers a new opportunity for yield estimation. The RSC dataset and source code are available online.

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