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Combined Use of FCN and Harris Corner Detection for Counting Wheat Ears in Field Conditions

文献类型： 外文期刊

作者： Wang, Daoyong 1 ; Fu, Yuanyuan 1 ; Yang, Guijun 1 ; Yang, Xiaodong 1 ; Liang, Dong 2 ; Zhou, Chengquan 3 ; Zhang, Ning 1 ;

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

2.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applic, Hefei 230601, Peoples R China

3.ZAAS, Inst Agr Equipment, Hangzhou 310021, Peoples R China

4.Inst Agr Sci Lixiahe Dist, Yangzhou 225100, Jiangsu, Peoples R China

关键词： Wheat-ear counting; fully convolutional network; wheat-ear adhesion; Harris corner detection; field conditions

期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）

ISSN： 2169-3536

年卷期： 2019 年 7 卷

页码：

收录情况： SCI

摘要： Accurate counting of wheat ears in field conditions is vital to predict yield and for crop breeding. To quickly and accurately obtain the number of wheat ears in a field, we propose herein a method to count wheat ears based on fully convolutional network (FCN) and Harris corner detection. The technical procedure consists essentially of 1) constructing a dataset of wheat-ear images from acquired red-green-blue (RGB) images; 2) training a FCN as the wheat-ear segmentation model by using the constructed image dataset; 3) preparing testing images and inputting them into the segmentation model to get the initial segmentation results; 4) binarizing the initial segmentation by using the Otsu algorithm (to facilitate subsequent processing); and 5) applying Harris corner detection after extracting the wheat-ear skeleton to obtain the number of wheat ears in the images. The segmentation results show that the proposed FCN-based segmentation model segments wheat ears with an average accuracy of 0.984 and at low computational cost. An average of only 0.033 s is required to segment a 256x256-pixel wheat-ear image. Moreover, the segmentation result is improved by nearly 10% compared with the previous segmentation methods under conditions of wheat-ear occlusion, leaf occlusion, uneven illumination, and soil disturbance. Subsequently, the proposed counting method achieves good results, with an average accuracy of 0.974, a coefficient of determination (R-2) of 0.983, and a root mean square error (RMSE) of 14.043. These metrics are all improved by 10% compared with the previous methods. These results show that the proposed method accurately counts wheat ears even under conditions of wheat-ear adhesion. Furthermore, the results provide an important technique for studying wheat phenotyping.

  • 相关文献

[1]Combined Use of FCN and Harris Corner Detection for Counting Wheat Ears in Field Conditions. Wang, Daoyong,Fu, Yuanyuan,Yang, Guijun,Yang, Xiaodong,Zhang, Ning,Wang, Daoyong,Liang, Dong,Zhang, Dongyan,Zhou, Chengquan,Wu, Hongya. 2019

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