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Automatic detection and counting of wheat spike based on DMseg-Count

文献类型： 外文期刊

作者： Zang, Hecang 1 ; Peng, Yilong 1 ; Zhou, Meng 1 ; Li, Guoqiang 1 ; Zheng, Guoqing 1 ; Shen, Hualei 3 ;

作者机构： 1.Henan Acad Agr Sci, Inst Agr Informat Technol, Zhengzhou 450002, Peoples R China

2.Minist Agr & Rural Areas, Huanghuaihai Key Lab Intelligent Agr Technol, Zhengzhou 450002, Peoples R China

3.Henan Normal Univ, Coll Comp & Informat Engn, Xinxiang 453007, Peoples R China

关键词： Field phenotyping; Wheat; Spike counting; Deep learning; Local segmentation branch

期刊名称：SCIENTIFIC REPORTS （ 影响因子：3.9； 五年影响因子：4.3 ）

ISSN： 2045-2322

年卷期： 2024 年 14 卷 1 期

页码：

收录情况： SCI

摘要： The automatic detection and counting of wheat spike images are of great significance for yield prediction and variety evaluation. Therefore, accurate and timely estimation of spike numbers is crucial for wheat production. However, in actual production, due to the susceptibility of wheat spike images to factors such as lighting conditions, shooting angles, occlusion, and overlap, the contour and features of wheat spike is unclear, which affects the accuracy of automatic detection and counting of wheat spike. In order to solve the above problems and further improve the accuracy of wheat spike counting, an improved wheat spike counting model DMseg-Count was proposed by enhancing local contextual supervision information based on existing target object counting model DM-Count. Firstly, wheat spike local segmentation branch was introduced to improve the network architecture of DM-Count, so as to extract the local contextual supervision information of wheat spike. Secondly, an element-by-element point multiplication mechanism was designed to fuse global and local contextual supervision information of wheat spike. Finally, the total loss function was constructed to optimize the model. The test results showed that the mean absolute error (MAE) and root mean square error (RMSE) of the proposed DMseg-Count model were 5.79 and 7.54, respectively, which were 9.76 and 10.91 higher than the standard distribution matching for crowd counting (DM-Count) model. Compared with other deep learning models, the proposed DMseg-Count model can detect wheat spike image in challenging situations, and has better computer vision processing capabilities and performance evaluation detection effect. In summary, the proposed DMseg-Count model can effectively detect wheat spike and has good counting performance, which provides a new method for automatic counting of wheat spike and yield prediction in complex field environments.

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