Integrated SKNet/Mobilenet V3 Classification of Mango Leaf Diseases and Infestations

SHEN Yihui; HE Huibin; CHEN Xiaoyu; YAN Shengnan

doi:10.19303/j.issn.1008-0384.2024.05.010

Volume 39 Issue 5

May 2024

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Article Navigation > Fujian Journal of Agricultural Sciences > 2024 > 39(5): 584-592

SHEN Y H, HE H B, CHEN X Y, et al. Integrated SKNet/Mobilenet V3 Classification of Mango Leaf Diseases and Infestations [J]. Fujian Journal of Agricultural Sciences，2024，39（5）：584−592 doi: 10.19303/j.issn.1008-0384.2024.05.010

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SHEN Y H, HE H B, CHEN X Y, et al. Integrated SKNet/Mobilenet V3 Classification of Mango Leaf Diseases and Infestations [J]. Fujian Journal of Agricultural Sciences，2024，39（5）：584−592 doi: 10.19303/j.issn.1008-0384.2024.05.010

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Integrated SKNet/Mobilenet V3 Classification of Mango Leaf Diseases and Infestations

doi: 10.19303/j.issn.1008-0384.2024.05.010

1.
College of Arts and Sciences, Fujian Medical University, Fuzhou, Fujian　350122, China
2.
Intelligent Technology Department, Fujian Agricultural Mechanization Research Institute, Fuzhou, Fujian　350005, China
3.
School of Computer and Control Engineering, Minjiang University, Fuzhou, Fujian　350103, China

Received Date: 2024-04-18
Rev Recd Date: 2024-05-05

Available Online: 2024-06-26

Publish Date: 2024-05-28

Abstract

Abstract

Objective Leaf diseases and infestations on mango trees were classified for database establishment and precision identification by combining the Mobilenet V3 model with Selective Kernel Network (SKNet). Method To improve the accuracy of disease and infestation classification on mango plants, data augmentation was firstly conducted. A denoising diffusion model was applied to expand the dataset followed by using a multi-scale structural similarity index to examine the similarity between the virtually generated and the camera-captured images of the diseases or infestations. Then, the training and generation effects of DDIM and DCGAN networks were compared. In the Mobilenet V3 model, the SE attention module was replaced with SKNet to construct the final platform. Results The MS-SSIM index of all types of DDIM images was greater than 0.63, which was higher than that of DCGAN. The classification accuracy of 98% delivered by merging SKNet with Mobilenet V3 was the best performance. Furthermore, combination of the two programs afforded more focus on the diseased leaves than did other smooth grade activation visualization by adding CA, CBAM, or ECA. Conclusion The newly developed classification method by integrating SKNet and Mobilenet V3 performed satisfactorily in distinguishing various diseased or infested mango leaves. The application not only significantly improved the efficiency and accuracy of disease identification but also reduced the epidemic monitoring costs by easily incorporating it with mobile or embedded devices.
- Mango leaf,
- diffusion probability model,
- Mobilenet,
- Selective Kernel Networks

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References(20)

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