|Table of Contents|

Grape Leaf Disease Recognition Method Based on SE-MobileNetV2

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2025年5
Page:
131-140
Research Field:
Publishing date:

Info

Title:
Grape Leaf Disease Recognition Method Based on SE-MobileNetV2
Author(s):
CHENG Zhenzhen1; ZHANG Bixiang2; CHENG Yifan3; MIAO Bailing1; GONG Shoufu1
(1.College of Horticulture，Xinyang Agriculture and Forestry College，Xinyang，Henan 464399；2.College of Horticulture，Huazhong Agricultural University，Wuhan，Hubei 430070；3.College of Optoelectronic Information Engineering，Huazhong University of Science and Technology，Wuhan，Hubei 430074)
Keywords:
grape; leaf disease; attention mechanism; image classification
PACS:
TP 391.9;S 431.9
DOI:
10.11937/bfyy.20243329
Abstract:
Taking four grape leaf diseases in the open data set of Plant Village as the test materials,a lightweight recognition method was proposed based on an improved MobileNetV2 model，aiming to achieve high-accuracy disease diagnosis while effectively reducing computational resource requirements.The method utilizes the lightweight MobileNetV2 framework and integrates the SE attention mechanism into the bottleneck layers，enhancing the model′s ability to focus on key features and further optimizing recognition performance while reducing the number of model parameters,in order to provide reference for the realization of high precision disease diagnosis,while effectively reducing the need for computing resourees.The results showed that the improved model achieved a recognition accuracy of 97.5% on the test set，which was 4.5% higher than the original MobileNetV2.Compared to ResNet50，ResNet34，and ShuffleNetV2，the proposed method improved the average accuracy by 10.2，18.7，and 28.2 percentage points，respectively，with a model size of only 20.7 MB.This study successfully balances computational cost and recognition accuracy，providing an effective solution for the challenge of grape leaf disease recognition.

References:

［1］王宇，杨畅，杨晓，等.5省市主栽葡萄的果实病害及防治调查［J］.植物保护，2021，47(3)：242-246.［2］李敏，闫成功，马帅，等．我国葡萄植保机械化技术发展现状与对策［J］．中国农机化学报，2024，45(1)：76-82．［3］蔺瑶，曾晏林，刘金涛，等．基于EBP-YOLOv8的葡萄叶病害检测与识别方法研究［J］．山东农业大学学报(自然科学版)，2024，55(3)：322-334．［4］郑建华，朱立学，朱蓉．基于多特征融合与支持向量机的葡萄病害识别［J］．现代农业装备，2018(6)：54-60．［5］栗苗苗．葡萄种植产业现状与发展对策探讨［J］．农业与技术，2020，40(11)：110-111．［6］刘媛，冯全．葡萄病害的计算机识别方法［J］．中国农机化学报，2017，38(4)：99-104．［7］田有文，李天来，李成华，等．基于支持向量机的葡萄病害图像识别方法［J］．农业工程学报，2007，23(6)：175-180．［8］胡施威，邓建新，王浩宇，等．基于改进EfficientNetB0模型的葡萄叶部病害识别方法［J］．现代电子技术，2024，47(15)：73-80．［9］乔虹，冯全，张芮，等．基于时序图像跟踪的葡萄叶片病害动态监测［J］．农业工程学报，2018，34(17)：167-175．［10］王超学，祁昕，马罡，等．基于YOLO V3的葡萄病害人工智能识别系统［J］．植物保护，2022，48(6)：278-288．［11］樊湘鹏,许燕,周建平,等.基于迁移学习和改进CNN的葡萄叶部病害检测系统［J］.农业工程学报,2021,37(6):151-159.［12］苏仕芳，乔焰，饶元．基于迁移学习的葡萄叶片病害识别及移动端应用［J］．农业工程学报，2021，37(10)：127-134．［13］何欣,李书琴,刘斌.基于多尺度残差神经网络的葡萄叶片病害识别［J］.计算机工程,2021,47(5):285-291,300.［14］谢圣桥，宋健，汤修映，等．基于迁移学习和残差网络的葡萄叶部病害识别［J］．农机化研究，2023，45(8)：18-23，28．［15］张林鍹,巴音塔娜,曾庆松.基于StyleGAN2-ADA和改进YOLO v7的葡萄叶片早期病害检测方法［J］.农业机械学报,2024,55(1):241-252.［16］SANDLER M，HOWARD A，ZHU M，et al．MobileNetV2：inverted residuals and linear bottlenecks［C］.Salt Lake City:CVPR,2018.［17］DONG X，ZHANG Z，YU R，et al．Extraction of information about individual trees from high-spatial-resolution UAV-acquired images of an orchard［J］．Remote Sensing，2020，12(1)：133．［18］JIEH，LI S，GANG S，ALBANIE S.Squeeze-and-excitation networks［C］.Salt Lake City:CVPR,2018.

Memo

Memo:
-
Last Update: 2025-03-14