Grape Disease Identification Method Based on YOLO v8-GSGF Model

ZHANG Huili, DAI Chenlong, REN Jinglong, WANG Guangyuan, TENG Fei, WANG Dongwei

Abstract

In order to further improve the accuracy and speed of grape disease identification, the YOLO v8 model was improved. Firstly, the GhostNetV2 backbone feature extraction network was introduced to improve the feature extraction ability and recognition performance of the model. Secondly, the SPPFCSPC pyramid pooling was embedded to improve the speed while keeping the receptive field unchanged. Thirdly, the GAM-Attention mechanism was added to reduce the information reduction and amplify the feature information to speed up the recognition. Finally, Focal-EIoU was used as the loss function to improve the bounding box regression performance of the detection model, and finally the grape leaf disease identification model YOLO v8-GSGF was formed. The recognition test verified that the YOLO v8-GSGF model can achieve 97.1% recognition accuracy and 45.3ms inference time, and can achieve high-precision identification of various grape diseases. The results of the ablation test showed that all the improvements had an effect on the recognition performance of the model, and the GhostNetV2 backbone network had the most obvious effect on the model. The YOLO v8-GSGF model can achieve 98.2% recognition accuracy and 43.7ms inference time in the ablation test, which was 8.6 percentage point and 20.4ms higher than that of the original YOLO v8 model. Compared with the current mainstream recognition model, the YOLO v8-GSGF model had better performance, better recognition accuracy and speed, and the curve chart also intuitively showed that the performance of the YOLO v8-GSGF model was superior, and the improvement effect was remarkable, which can meet the needs of grape orchard disease identification and had the potential for practical application.

 

Keyword: grape leaves ; disease ; image recognition ; GhostNetV2 ; YOLO v8

 

Download Full Text:

PDF


References


ZHAO Hui, HUANG Biao, WANG Hongjun, et al. Research on pest identification algorithm based on improved YOLO v7 in complex farmland environment[ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54( 10) ; 246 -254. (in Chinese)

JIA Lu, YE Zhonghua. Grape disease identification model based on attention mechanism and feature fusion [J ]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(7) ; 223 -233. (in Chinese)

LIANC Q K, XIANC S, HU Y C, et al. ( PDSE) — S—2 — Net: computer-assisted plant disease diagnosis and severity estimation network [J ]. Computers and Electronics in Agriculture, 2019, 157; 518 -529.

GANDHI R, NIMBALKAR S, YELAMANCHILI N, et al. Plant disease detection using CNNs and GANs as an augmentative approach [С] //2018 IEEE International Conference on Innovative Research and Development (ICIRD). IEEE, 2018: 1 -5.

SUN Fenggang, WANG Yunlu, LAN Peng, et al. An apple fruit disease identification method based on improved YOLO v5s and transfer learning[J ]. Transactions of the CSAE ,2022 ,38 ( 11 ) : 171 - 179. (in Chinese)

HAN L X, HALEEM M S, TAYLOR M, et al. A novel computer vision-based approach to automatic detection and severity assessment of crop diseases[ С ]//Science and Information Conference (SAI) , 2015;638 -644.

ZHANG С L, ZHANG S W, YANC J C, et al. Apple leaf disease identification using genetic algorithm and correlation based feature selection method [ J]. International Journal of Agricultural and Biological Engineering, 2017, 10(2) :74 -83.

REHMAN Z,KHAN M A, AHMED F,et al. Recognizing appleleaf diseases using a novel parallel real-time processing framework based on MASK RCNN and transfer learning: an application for smart agriculture[ J ]. IET Image Processing,2021 ,15(10) : 2157 -2168.

FAN Xiangpeng, ZHOU Jianping, XU Yan, et al. Corn disease recognition under complicated background based on improved convolutional neural network [ J]. Transactions of the Chinese Society for Agricultural Machinery, 2021 , 52(3) *.210 -217. (in Chinese)

KRIZHEVSKY A, SUTSKEVER I, HINTON G E. I mage net classification with deep convolutional neural networks [ J ]. Advances in Neural Information Processing Systems,2012, 25: 1097 - 1105.

HE К M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition С ] //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ,2016: 770 -778.

REDMON J, DIVVALA S, GIRSIIICK R, el al. You only look once: unified, real-time object detection[ [С]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016; 779 -788.

SANDLER M, HOWARD A, ZHU M L, et al. MobileNetV2: inverted residuals and linear botlenecks[ С ] //31 st IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR) , 2018:4510 -4520.

MA N N, ZHANG X Y, ZHENC H T, et al. ShufleNet V2; practical guidelines for efficient CNN architecture design [С] // 15th European Conference on Computer Vision (ECCV) ,2018: 122 - 138.

TANG Y, HAN K, GUO J, et al. GhostNetV2: enhance cheap operation with long-range attention J j. arXiv Preprint, arXiv; 2211.12905, 2022.

HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9) : 1904 - 1916.

LIU Y ,SHAO Z, HOFFMANN N. Global attention mechanism: retain information to enhance channel-spatial interactions [J]. arXiv Preprint, arXiv;2112. 05561 ,2021.

ZHANG Y F, REN W, ZHANG Z, et al. Focal and efficient IOU loss for bounding accurate box regression [ J]. Neurocomputing,2022,506 : 146 - 157.

LIU ^ang, FENG Quan, WANG Shuzhi. Plant disease identification method based on light weight CNN and mobile application [J] . Transactions of the CSAE ,2019 ,35(17): 194 - 204. (in Chinese)

XU Jinghui, SHAO Mingye, WANG Yichen, et al. Recognition of corn leaf spot and rust based on transfer learning with convolutional neural network [ J]. Transactions of the Chinese Society for Agricultural Machinery, 2020,51(2) : 230 -236. (in Chinese)

W ANG X, LIU J. Tomato anomalies detection in greenhouse scenarios based on yolo-dense[ J ]. Frontiers in Plant Science, 2021,12: 533.

CHEN J D, WANG W H,ZHANG D F, et al. Attention embedded lightweight network for maize disease recognition [J]. Plant Pathology,2021 ,70(3) : 630 -642.

ZHANG S W , ZHANG S B, ZHANG С L, et al. Cucumber leaf disease identification with global pooling dilated convolutional neural network [J] . Computers and Electronics in Agriculture,2019 ,162 ;422 -430.

HUGHES D,SALATHE M. An open access repository of imageson plant health to enable the development of mobile disease diagnostics [ EB/OL]. (2016 —04 - 12 ) 2024—01 -01 . https://arxiv.org/abs/1511.08060.

THAPA R, SNAVELY N, BELONGIE S, et al. The plant pathology 2020 challenge dataset to classify foliar disease of apples [EB/OL]. (2020-04 -24) [2024-01 -01]. https://arxiv.org/abs/2004. 11958.

AiStudio. Pathological image of apple leaves EB/OL j .(2019 — 11 — 17) 2024 — 01 — 01 . https; // aistudio. baidu. com/ aistudio/datasetdetail/1 1591/0.


Refbacks

  • There are currently no refbacks.