[1]唐义声,姜新华,于晓芳,等.基于WPM-YOLO11的玉米叶片病害检测方法[J].江苏农业学报,2026,42(02):264-272.[doi:doi:10.3969/j.issn.1000-4440.2026.02.005]
　TANG Yisheng,JIANG Xinhua,YU Xiaofang,et al.A maize leaf disease detection method based on WPM-YOLO11[J].,2026,42(02):264-272.[doi:doi:10.3969/j.issn.1000-4440.2026.02.005]
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基于WPM-YOLO11的玉米叶片病害检测方法()

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
42
期数:
2026年02期
页码:
264-272
栏目:
植物保护
出版日期:
2026-02-28

文章信息/Info

Title:
A maize leaf disease detection method based on WPM-YOLO11
作者:
唐义声1; 2; 姜新华1; 2; 于晓芳3; 高聚林3; 胡健1; 2; 张子汉1; 2; 翟成珺4
(1.内蒙古农业大学计算机与信息工程学院,内蒙古呼和浩特010018；2.内蒙古自治区农牧业大数据研究与应用重点实验室,内蒙古呼和浩特010018；3.内蒙古农业大学农学院,内蒙古呼和浩特010018；4.内蒙古自治区教育考试院，内蒙古呼和浩特010011)
Author(s):
TANG Yisheng1; 2; JIANG Xinhua1; 2; YU Xiaofang3; GAO Julin3; HU Jian1; 2; ZHANG Zihan1; 2; ZHAI Chengjun4
(1.College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China；2.Inner Mongolia Key Laboratory of Agricultural and Animal Husbandry Big Data Research and Application, Hohhot 010018, China；3.College of Agronomy, Inner Mongolia Agricultural University, Hohhot 010018, China；4.Education Examinations Authority of Inner Mongolia Autonomous Region, Hohhot 010011, China)
关键词:
玉米叶片病害; 目标检测; YOLOv11; 多尺度特征融合; 注意力机制
Keywords:
maize leaf disease; object detection; YOLOv11; multi-scale feature fusion; attention mechanism
分类号:
S435.131
DOI:
doi:10.3969/j.issn.1000-4440.2026.02.005
文献标志码:
A
摘要:
玉米作为全球重要的粮食作物,其产量与质量易受多种叶片病害影响。针对玉米病害病斑边缘模糊、小目标检测困难等问题,本研究提出基于YOLOv11m改进的WPM-YOLO11模型。该模型引入风车状方向卷积(Windmill convolution)模块以增强边缘与方向特征提取能力,设计多尺度增强并行注意力(MSEPA)模块提升多尺度信息融合效率。数据集构建涵盖感染褐斑病、普通锈病、南方锈病、花叶病、北方枯叶病、灰斑病、圆斑病的玉米叶片及健康玉米叶片,结果表明,WPM-YOLO11模型交并比阈值为0.50的平均精度均值(mAP50)和交并比阈值为0.50～0.90的平均精度均值(mAP50∶95)分别达到93.7%和78.5%,整体性能优于YOLOv11m及其他主流模型。本研究为玉米病害智能识别提供了技术方案,对智慧农业病害监测与精准防治具有重要意义。
Abstract:
As a globally important food crop, maize is vulnerable to various leaf diseases that affect its yield and quality. To address issues such as blurred edges of maize lesions and difficulties in small target detection, this study proposed the WPM-YOLO11 model improved based on YOLOv11m. The model introduced a windmill convolution module to enhance the ability of edge and directional feature extraction, and designed a multi-scale enhanced parallel attention (MSEPA) module to improve the efficiency of multi-scale information fusion. The constructed dataset included maize leaves infected with brown spot, common rust, southern rust, mosaic disease, northern leaf blight, gray leaf spot, circular spot, as well as healthy maize leaves. The results showed that the mean average precision at an intersection over union (IoU) threshold of 0.50 (mAP50) and the mean average precision at IoU thresholds from 0.50 to 0.90 (mAP50∶95) of the WPM-YOLO11 model reached 93.7% and 78.5%, respectively, and its overall performance was superior to that of YOLOv11m and other mainstream models. This study provides a technical solution for the intelligent identification of maize diseases, and is of great significance for disease monitoring and precise control in smart agriculture.

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备注/Memo

备注/Memo:
收稿日期：2025-06-18基金项目：内蒙古自然科学基金项目(2023LHMS06017、2024LHMS06021)；国家自然科学基金项目(62061037、31960494)；内蒙古自治区科技攻关计划项目(2020GG0169)；内蒙古自治区科技重大专项(2021ZD0003)作者简介：唐义声(1998-),男,辽宁辽阳人,硕士研究生,研究方向为模式识别与智能信息处理。(E-mail)easontown@foxmail.com通讯作者：姜新华,(E-mail) jiangxh@imau.edu.cn
更新日期/Last Update: 2026-03-16