https://doi.org/10.4081/jae.2024.1587
Detection method of potato leaf disease based on YOLOv5s
Published: 3 June 2024
Abstract Views: 463
PDF: 267
HTML: 11
HTML: 11
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
An improved leaf target detection method based on the YOLOv5s network is proposed to address the issues of low model detection accuracy and slow detection speed in potato leaf image target detection. Firstly, a deformable convolution replaces the standard convolution in YOLOv5s to ensure that the convolution region always covers the target region. Secondly, CBAM attention module is introduced into the convolutional module to enhance local feature extraction and fusion capability of the network, while WIoU_Loss serves as Bounding box loss function SRN-DeblurNet deblurnet is combined with YOLOv5s network to convert part of fuzzy images into clear ones before being integrated with multi-scale features for model prediction. To verify its effectiveness, we trained our model using Pytorch deep learning framework and achieved an accuracy rate of 90.3% and recall rate of 88%, which are respectively 8.6% and 8.9% higher than those obtained by YOLOv5s.
Huang C., Liu W.C. 2016. Occurrence characteristice and monitoring advice of potato late blight in China in recent years. Plant Protection. 42(5):142－147.
Li Y.H., Huo C., Cao J.F. 2022. Epidemics and infection characteristics of potato late blight in different seasons in Yunnan province. Southwest China Journal of Agricultural Sciences. 35(09):2046-2053.
Xi R., Jiang K.,Zhang W.Z. 2020. Recognition method for potato buds based on improved faster R-CNN. Transactions of the Chinese Society for Agricultural Machinery. 51(4): 216-223.
Yang S., Feng Q., Zhang J.H. 2020. Identification method for potato disease based on deep learning and composite dictionary. Transactions of the Chinese Society of Agricultural Machinery. 51(7):22-29.
Peng H.X., Huang B., Shao Y.Y. 2018. General improved SSD model for picking object recognition of multiple fruits in natural environment. Transactions of the Chinese Society of Agricultural Engineering. 34(16): 155-162.
Xiu C.B., Sun L.L. 2022. Potato Leaf Bud Detection Method Based on Improved YOLO v4 Network. Transactions of the Chinese Society for Agricultural Machinery. 53(6):265-273.
Zhang Z.G., Zhang Z.D., Li J.N. 2021. Potato detection in complex environment based on improved YoloV4 model. Transactions of the Chinese Society of Agricultural Engineering. 37(22): 170-178.
Cai S.P., Pan W.H., Liu H. 2023. Orchard Obstacle Detection Based on D2-YOLO Deblurring Recognition Network.Transactions of the Chinese Society for Agricultural Machinery. 54(2):284-292.
Wang X.Y., Li Y.X., Yang Z.Y. 2021. Detection Method of Clods and Stones from Impurified Potatoes Based on Improved YOLO v4 Algorithm. Transactions of the Chinese Society for Agricultural Machinery. 52(08):241-247+262.
Fan Z.J., Li X.X. 2019. Recognition of potato diseases based on fast detection and fusion features of ROI. Southwest China Journal of Agricultural Sciences. 32(3): 544-550.
Dang M.Y., Meng Q.K., Gu F., Gu B., Hu Y.H. 2020. Rapid recognition of potato late blight based on machine vision.Transactions of the Chinese Society of Agricultural Engineering.36(2):193-200.
Xiao Z.Y., Liu H. 2017. Adaptive features fusion and fast recognition of potato typical disease images. Transactions of the Chinese Society for Agricultural Machinery. 48(12): 26-32.
Li J.H., Lin L.J., Tian K. 2020. Detection of leaf diseases of balsam pear in the field based on improved Faster R-CNN. Transactions of the Chinese Society of Agricultural Engineering. 36(12): 179-185.
Lin T.Y., Dollar P., Girshick R. 2017. Feature Pyramid Networks for Object Detection.IEEE Computer Society：936-944.
DOI: https://doi.org/10.1109/CVPR.2017.106
Dai J., Qi H., Xiong Y. 2017. Deformable Convolutional Networks.IEEE:764-773.
DOI: https://doi.org/10.1109/ICCV.2017.89
Zhu X., Hu H., Lin S. 2020. Deformable ConvNets V2: More Deformable,Better Results//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE, 2020：9300-9308.
DOI: https://doi.org/10.1109/CVPR.2019.00953
Wang W.H., Dai J.F., Chen Z., Huang Z.H., Li Z.Q., Zhu X.Z., Hu X.W., Lu T., Lu L.W., Li H.S. 2023. Internimage: Exploring large-scale vision foundation models with deformable convolutions. In Proceedings of the IEEE/CVF International Conference on Computer Vision.
DOI: https://doi.org/10.1109/CVPR52729.2023.01385
Woo S., Park J., Lee J Y. 2018. Cbam : Convolutional block attention module / / Proceedings of the
DOI: https://doi.org/10.1007/978-3-030-01234-2_1
European Conference on Computer Vision (ECCV): 3-19.
Zheng Z.H., Wang P., Liu W., Li J.Z., Ye R.G., Ren D.W. 2020. Distance-IoU loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), volume 34, pages 12993–13000.
DOI: https://doi.org/10.1609/aaai.v34i07.6999
Tong Z., Chen Y., Xu Z. 2023. Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism. arXiv preprint arXiv:2301.10051.
Tao X., Gao H., Wang Y. 2018. Scale-recurrent Network for Deep Image Deblurring//2018 IEEE/CVF Conference on Computer Vision and PatternRecognition.IEEE:8174-8182.
DOI: https://doi.org/10.1109/CVPR.2018.00853
Xue J.L., Li Y.Q., Cao Z.J. 2022. Obstacle Detection Based on Deep Learning for Blurred Farmland Images.Transactions of the Chinese Society for Agricultural Machinery. 53(3):234-242.
Li H., Yan K.H., Jing H. 2022. Pathological detection and identification of apple leaves based on improved SSD.Sensors and microsystems. 41(10):134-137.
Li T., Ren L., Hu B. 2023. Grading detection of tomato hole-pan seedlings using improved YOLOv5s and transferlearning. Transactions of the Chinese Society of Agricultural Engineering. 39(23):174-184.
Zuo H.X., Huang Q.C., Yang J.H. 2023. Improved YOLOv5s-based detection method for tomato yellow leaf curl virus disease. Transactions of the Chinese Society for Agricultural Machinery. 1-11.
Sun G.F., Wang Y.L., Lan P. 2022. Identification of apple fruit diseases using improved YOLOv5s and transferlearning. Transactions of the Chinese Society of Agricultural Engineering. 38(11):171-179.
Supporting Agencies
Major Science and Technology Special Project, Yunnan Provincial Department of Science and TechnologyHow to Cite
Li, J. (2024) “Detection method of potato leaf disease based on YOLOv5s”, Journal of Agricultural Engineering, 55(3). doi: 10.4081/jae.2024.1587.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Simona M.C. Porto, Giulia Castagnolo, Francesca Valenti, Giovanni Cascone, Kernel density estimation analyses based on a low power-global positioning system for monitoring environmental issues of grazing cattle , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
- Andrea De Montis, Amedeo Ganciu, Fabio Recanatesi, Antonio Ledda, Vittorio Serra, Mario Barra, Stefano De Montis, The scientific production of Italian agricultural engineers: a bibliometric network analysis concerning the scientific sector AGR/10 Rural buildings and agro-forestry territory , Journal of Agricultural Engineering: Vol. 48 No. s1 (2017): Special Issue
- Pankaj Tyagi, Rahul Semwal, Anju Sharma, Uma Shanker Tiwary, Pritish Varadwaj, E-nose: a low-cost fruit ripeness monitoring system , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- Fuhao Zhu, Jin Chen, Zhuohuai Guan, Yahui Zhu, Hao Shi, Kai Cheng, Development of a combined harvester navigation control system based on visual simultaneous localization and mapping-inertial guidance fusion , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- Meriem Er-Rami, Guido D'Urso, Nicola Lamaddalena, Daniela D'Agostino, Oscar Rosario Belfiore, Analysis of irrigation system performance based on an integrated approach with Sentinel-2 satellite images , Journal of Agricultural Engineering: Vol. 52 No. 2 (2021)
- Simone Pascuzzi, Emanuele Cerruto, An innovative pneumatic electrostatic sprayer useful for tendone vineyards , Journal of Agricultural Engineering: Vol. 46 No. 3 (2015)
- Giuseppe Modica, Francesco Solano, Angelo Merlino, Salvatore Di Fazio, Francesco Barreca, Luigi Laudari, Carmelo Riccardo Fichera, Using Landsat 8 imagery in detecting cork oak (Quercus suber L.) woodlands: a case study in Calabria (Italy) , Journal of Agricultural Engineering: Vol. 47 No. 4 (2016)
- Gianfranco Pergher, Nicola Zucchiatti, Influence of canopy development in the vineyard on spray deposition from a tunnel sprayer , Journal of Agricultural Engineering: Vol. 49 No. 3 (2018)
- Stefania Pindozzi, Elena Cervelli, Pier Francesco Recchi, Alessandra Capolupo, Lorenzo Boccia, Predicting land use change on a broad area: Dyna-CLUE model application to the Litorale Domizio-Agro Aversano (Campania, South Italy) , Journal of Agricultural Engineering: Vol. 48 No. s1 (2017): Special Issue
- Marko Milan Kostić, Nataša Ljubičić, Vladimir Aćin, Milan Mirosavljević, Maša Budjen, Miloš Rajković, Nebojša Dedović, An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
