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

SHEN Yihui; HE Huibin; CHEN Xiaoyu; YAN Shengnan

Article Contents

Article Navigation > Fujian Journal of Agricultural Sciences > 2024 >

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）：1−9

Citation:

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）：1−9

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）：1−9

Citation:

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）：1−9

PDF( 3363 KB)

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

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

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

FullText(HTML)

References(20)

References

[1]	杨芝霓. 芒果主要采后病害的病原鉴定及炭疽病的生物防治[D]. 南宁: 广西大学. YANG Z N. Pathogen identification of mango post havest disease and biological control of anthracnose[D]. Nanning: Guangxi University. (in Chinese)
[2]	秦丰, 刘东霞, 孙炳达, 等. 基于图像处理技术的四种苜蓿叶部病害的识别 [J]. 中国农业大学学报, 2016, 21(10):65−75. QIN F, LIU D X, SUN B D, et al. Recognition of four different alfalfa leaf diseases based on image processing technology [J]. Journal of China Agricultural University, 2016, 21(10): 65−75. (in Chinese)
[3]	柴阿丽, 李宝聚, 石延霞, 等. 基于计算机视觉技术的番茄叶部病害识别 [J]. 园艺学报, 2010, 37(9):1423−1430. CHAI A L, LI B J, SHI Y X, et al. Recognition of tomato foliage disease based on computer vision technology [J]. Acta Horticulturae Sinica, 2010, 37(9): 1423−1430. (in Chinese)
[4]	SHI B Z, ZHOU X L, QIN Z K, et al. Corn ear quality recognition based on DCGAN data enhancement and transfer learning[C]//The 4th International Conference on Electronics, Communications and Control Engineering. Seoul Republic of Korea. ACM, 2021.
[5]	AZIZI S, KORNBLITH S, SAHARIA C, et al. Synthetic data from diffusion models improves imagenet classification [J]. arXiv preprint arXiv:2304.08466, 2023.
[6]	哈马友吉, 任万春, 张秤, 等. 基于轻量级网络MobileNet V2的二极管玻壳缺陷识别 [J]. 传感器与微系统, 2022, 41(4):153−155,160. HAMA Y J, REN W C, ZHANG C, et al. Defect recognition of diode glass shells based on lightweight network MobileNet V2 [J]. Transducer and Microsystem Technologies, 2022, 41(4): 153−155,160. (in Chinese)
[7]	王志强, 于雪莹, 杨晓婧, 等. 基于WGAN和MCA-MobileNet的番茄叶片病害识别 [J]. 农业机械学报, 2023, 54(5):244−252. WANG Z Q, YU X Y, YANG X J, et al. Tomato leaf diseases recognition based on WGAN and MCA-MobileNet [J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(5): 244−252. (in Chinese)
[8]	LI X, WANG W H, HU X L, et al. Selective kernel networks[J]. IEEE, 2020. DOI: 10.1109/CVPR.2019.00060.
[9]	AHMED S I, IBRAHIM M, NADIM M, et al. MangoLeafBD: A comprehensive image dataset to classify diseased and healthy mango leaves [J]. Data in Brief, 2023, 47: 108941. doi: 10.1016/j.dib.2023.108941
[10]	ZHANG Q S, TAO M L, CHEN Y X. gDDIM: Generalized denoising diffusion implicit models [J]. arXiv preprint arXiv: 2206.05564v1, 2022.
[11]	闫志浩, 周长兵, 李小翠. 生成扩散模型研究综述 [J]. 计算机科学, 2024, 51(1):273−283. YAN Z H, ZHOU C B, LI X C. Survey on generative diffusion model [J]. Computer Science, 2024, 51(1): 273−283. (in Chinese)
[12]	WANG Z, SIMONCELLI E P, BOVIK A C. Multiscale structural similarity for image quality assessment[C]//The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003. Pacific Grove, CA, USA. IEEE, 2003: 1398-1402.
[13]	HOWARD A G, ZHU M L, CHEN B, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications [J]. arXiv preprint arXiv:1704.04861, 2017.
[14]	SANDLER M, HOWARD A, ZHU M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23, 2018. Salt Lake City, UT. IEEE, 2018: 4510-4520.
[15]	HOWARD A, SANDLER M, CHEN B, et al. Searching for MobileNetV3[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). October 27-November 2, 2019. Seoul, Korea (South). IEEE, 2019: 1314-1324.
[16]	刘勇国, 高攀, 兰荻, 等. ECA-SKNet: 玉米单倍体种子的卷积神经网络识别模型 [J]. 电子科技大学学报, 2023, 52(6):866−871. LIU Y G, GAO P, LAN D, et al. ECA-SKNet: Convolutional neural network identification model for corn haploid seeds [J]. Journal of University of Electronic Science and Technology of China, 2023, 52(6): 866−871. (in Chinese)
[17]	何翔. 基于DCGANs的半片光伏组件电致发光图像增强技术 [J]. 应用光学, 2023, 44(2):314−322. doi: 10.5768/JAO202344.0202003 HE X. Electroluminescence image enhancement technology of half-cut photovoltaic module based on DCGANs [J]. Journal of Applied Optics, 2023, 44(2): 314−322. (in Chinese) doi: 10.5768/JAO202344.0202003
[18]	HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 20-25, 2021. Nashville, TN, USA. IEEE, 2021: 13713-13722.
[19]	WANG Q L, WU B G, ZHU P F, et al. ECA-net: Efficient channel attention for deep convolutional neural networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 13-19, 2020. Seattle, WA, USA. IEEE, 2020: 11534-11542.
[20]	WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[M]//Computer Vision – ECCV 2018. Cham: Springer International Publishing, 2018: 3-19.