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Crop Disease Detection against Complex Background Based on Improved Atrous Spatial Pyramid Pooling

文献类型： 外文期刊

作者： Ma, Wei 1 ; Yu, Helong 3 ; Fang, Wenbo 2 ; Guan, Fachun 2 ; Ma, Dianrong 1 ; Guo, Yonggang 4 ; Zhang, Zhengchao 2 ; Wang, Chao 2 ;

作者机构： 1.Shenyang Agr Univ, Rice Res Inst, Shenyang 110000, Peoples R China

2.Jilin Acad Agr Sci, Changchun 130033, Peoples R China

3.Jilin Agr Univ, Inst Smart Agr, Changchun 130018, Peoples R China

4.Tibet Agr & Anim Husb Univ, Water Conservancy Project & Civil Engn Coll, Linzhi 860000, Peoples R China

关键词： disease; dual attention; dilated convolution; machine learning

期刊名称：ELECTRONICS （ 影响因子：2.9； 五年影响因子：2.9 ）

ISSN：

年卷期： 2023 年 12 卷 1 期

页码：

收录情况： SCI

摘要： Timely crop disease detection, pathogen identification, and infestation severity assessments can aid disease prevention and control efforts to mitigate crop-yield decline. However, improved disease monitoring methods are needed that can extract high-resolution, accurate, and rich color and spatial features from leaf disease spots in the field to achieve precise fine-grained disease-severity classification and sensitive disease-recognition accuracy. Here, we propose a neural-network-based method incorporating an improved Rouse spatial pyramid pooling strategy to achieve crop disease detection against a complex background. For neural network construction, first, a dual-attention module was introduced into the cross-stage partial network backbone to enable extraction of multi-dimensional disease information from the channel and space perspectives. Next, a dilated convolution-based spatial pyramid pooling module was integrated within the network to broaden the scope of the collection of crop-disease-related information from images of crops in the field. The neural network was tested using a set of sample data constructed from images collected at a rate of 40 frames per second that occupied only 17.12 MB of storage space. Field data analysis conducted using the miniaturized model revealed an average precision rate approaching 90.15% that exceeded the corresponding rates obtained using comparable conventional methods. Collectively, these results indicate that the proposed neural network model simplified disease-recognition tasks and suppressed noise transmission to achieve a greater accuracy rate than is obtainable using similar conventional methods, thus demonstrating that the proposed method should be suitable for use in practical applications related to crop disease recognition.

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