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Ds-Menet For The Classification Of Citrus Disease, Xuyao Liu, Yaowen Hu, Guoxiong Zhou, Weiwei Cai, Mingfang He, Jialei Zhan, Yahui Hu, Liujun Li
Ds-Menet For The Classification Of Citrus Disease, Xuyao Liu, Yaowen Hu, Guoxiong Zhou, Weiwei Cai, Mingfang He, Jialei Zhan, Yahui Hu, Liujun Li
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish …
Casm-Amfmnet: A Network Based On Coordinate Attention Shuffle Mechanism And Asymmetric Multi-Scale Fusion Module For Classification Of Grape Leaf Diseases, Jiayu Suo, Jialei Zhan, Guoxiong Zhou, Aibin Chen, Yaowen Hu, Weiqi Huang, Weiwei Cai, Yahui Hu, Liujun Li
Casm-Amfmnet: A Network Based On Coordinate Attention Shuffle Mechanism And Asymmetric Multi-Scale Fusion Module For Classification Of Grape Leaf Diseases, Jiayu Suo, Jialei Zhan, Guoxiong Zhou, Aibin Chen, Yaowen Hu, Weiqi Huang, Weiwei Cai, Yahui Hu, Liujun Li
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale …