Classification of Fine-Grained Crop Disease by Dilated Convolution and Improved Channel Attention Module
文献类型：期刊论文
| 作者 | Zhang, Xiang2,3; Gao, Huiyi1,3 ; Wan, Li1,3
|
| 刊名 | AGRICULTURE-BASEL
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 12 |
| 关键词 | fine-grained crop disease convolutional neural networks attention mechanism classification |
| DOI | 10.3390/agriculture12101727 |
| 通讯作者 | Gao, Huiyi(hygao@iim.ac.cn) |
| 英文摘要 | Crop disease seriously affects food security and causes huge economic losses. In recent years, the technology of computer vision based on convolutional neural networks (CNNs) has been widely used to classify crop disease. However, the classification of fine-grained crop disease is still a challenging task due to the difficult identification of representative disease characteristics. We consider that the key to fine-grained crop disease identification lies in expanding the effective receptive field of the network and filtering key features. In this paper, a novel module (DC-DPCA) for fine-grained crop disease classification was proposed. DC-DPCA consists of two main components: (1) dilated convolution block, and (2) dual-pooling channel attention module. Specifically, the dilated convolution block is designed to expand the effective receptive field of the network, allowing the network to acquire information from a larger range of images, and to provide effective information input to the dual-pooling channel attention module. The dual-pooling channel attention module can filter out discriminative features more effectively by combining two pooling operations and constructing correlations between global and local information. The experimental results show that compared with the original networks (85.38%, 83.22%, 83.85%, 84.60%), ResNet50, VGG16, MobileNetV2, and InceptionV3 embedded with the DC-DPCA module obtained higher accuracy (87.14%, 86.26%, 86.24%, and 86.77%). We also provide three visualization methods to fully validate the rationality and effectiveness of the proposed method in this paper. These findings are crucial by effectively improving classification ability of fine-grained crop disease by CNNs. Moreover, the DC-DPCA module can be easily embedded into a variety of network structures with minimal time cost and memory cost, which contributes to the realization of smart agriculture. |
| WOS关键词 | IDENTIFICATION |
| 资助项目 | Key Research and Development Plan of Anhui Province[202004e11020010] ; Key Research and Development Plan of Anhui Province[202104a06020025] |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000872100000001 |
| 资助机构 | Key Research and Development Plan of Anhui Province |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/129845]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Gao, Huiyi |
| 作者单位 | 1.Anhui Inst Innovat Ind Technol, Luan Branch, Luan 237100, Peoples R China 2.USTC, Sci Isl Branch, Grad Sch, Hefei 230026, Peoples R China 3.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Xiang,Gao, Huiyi,Wan, Li. Classification of Fine-Grained Crop Disease by Dilated Convolution and Improved Channel Attention Module[J]. AGRICULTURE-BASEL,2022,12. |
| APA | Zhang, Xiang,Gao, Huiyi,&Wan, Li.(2022).Classification of Fine-Grained Crop Disease by Dilated Convolution and Improved Channel Attention Module.AGRICULTURE-BASEL,12. |
| MLA | Zhang, Xiang,et al."Classification of Fine-Grained Crop Disease by Dilated Convolution and Improved Channel Attention Module".AGRICULTURE-BASEL 12(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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