[1]王少芳,陈贤,崔艳荣,等.基于HTWYOLO的轻量化草莓病害检测方法[J].江苏农业科学,2025,53(20):262-271.
　Wang Shaofang,et al.A lightweight strawberry disease detection method based on HTWYOLO[J].Jiangsu Agricultural Sciences,2025,53(20):262-271.
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基于HTWYOLO的轻量化草莓病害检测方法()

《江苏农业科学》[ISSN:1002-1302/CN:32-1214/S]

卷:
第53卷
期数:
2025年第20期
页码:
262-271
栏目:
病虫害智能检测
出版日期:
2025-10-20

文章信息/Info

Title:
A lightweight strawberry disease detection method based on HTWYOLO
作者:
王少芳; 陈贤; 崔艳荣; 陈中举; 詹炜
长江大学计算机科学学院，湖北荆州 434023
Author(s):
Wang Shaofang; et al
关键词:
草莓病害; YOLO v8; 目标检测; 轻量化; 深度学习; HTW-YOLO
Keywords:
-
分类号:
TP391.41；S126
DOI:
-
文献标志码:
A
摘要:
针对草莓容易受到多种病害侵袭，且病害形态各异，传播速度快，导致草莓病害检测效率低下、检测精度不高等问题，提出一种基于YOLO v8s改进的HTW-YOLO轻量化草莓病害检测模型。首先，将颈部网络替换为高层次特征筛选融合金字塔网络，通过高层特征筛选低级特征信息的多层次筛选和融合，以增强模型对病害特征信息的捕捉能力，并融合坐标注意力机制，进一步提高模型对病害的定位和识别；其次，采用任务对齐检测头，以共享卷积进行特征提取，降低检测头参数的同时，利用病害的分类定位特征交互信息，提高模型的病害检测性能；最后，将原损失函数优化为WIoU损失函数，增强模型的泛化能力，使模型在各个优劣样本中取得平衡，提高整体检测性能。结果表明，所提出的模型检测精确率为94.0%，召回率为89.8%，平均精度均值为94.5%，参数量和模型大小分别为61、12.4 M，与基准模型YOLO v8s相比，在参数量和模型大小分别降低45.0%、45.1%的情况下，精确率、召回率和平均精度值分别提升2.6、3.2、4.3百分点。改进的模型与Faster R-CNN、YOLO v5s、YOLO v7-tiny、YOLO v9s、YOLO v10s相比，仅模型参数量和大小略高于YOLO v7-tiny，精确率比YOLO v9s低0.5百分点，其他性能均为最优，在实现模型轻量化的同时取得了较高的草莓病害识别能力。
Abstract:
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参考文献/References:

［1］陈迪娟，成英. 赣榆区草莓发展现状及对策［J］. 农业技术与装备，2019(6)：91，93.
［2］Ji Y，Li X，Gao Q H，et al. Colletotrichum species pathogenic to strawberry：discovery history，global diversity，prevalence in China，and the host range of top two species［J］. Phytopathology Research，2022，4(1)：4.
［3］杨涵，关统伟，徐红星，等. 草莓有害病毒种类与防治技术［J］. 现代农业科技，2020(2)：88-91.
［4］蔺瑶，曾晏林，刘金涛，等. 基于EBP-YOLO v8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.
［5］Dargan S，Kumar M，Ayyagari M R，et al. A survey of deep learning and its applications：a new paradigm to machine learning［J］. Archives of Computational Methods in Engineering，2020，27(4)：1071-1092.
［6］石晶晶，刘占宇，张莉丽，等. 基于支持向量机(SVM)的稻纵卷叶螟危害水稻高光谱遥感识别［J］. 中国水稻科学，2009，23(3)：331-334.
［7］李冠林，马占鸿，黄冲，等. 基于K-means硬聚类算法的葡萄病害彩色图像分割方法［J］. 农业工程学报，2010，26(增刊2)：32-37.
［8］Ferentinos K P. Deep learning models for plant disease detection and diagnosis［J］. Computers and Electronics in Agriculture，2018，145：311-318.
［9］李滨，樊健. 基于YOLO v5的水稻害虫分类［J］. 江苏农业科学，2024，52(2)：175-182.
［10］Anandhakrishnan T，Jaisakthi S M. Deep Convolutional Neural Networks for image based tomato leaf disease detection［J］. Sustainable Chemistry and Pharmacy，2022，30：100793.
［11］Nie X，Wang L Y，Ding H X，et al. Strawberry Verticillium wilt detection network based on multi-task learning and attention［J］. IEEE Access，2019，7：170003-170011.
［12］刘小刚，范诚，李加念，等. 基于卷积神经网络的草莓识别方法［J］. 农业机械学报，2020，51(2)：237-244.
［13］Li Y，Wang J C，Wu H R，et al. Detection of powdery mildew on strawberry leaves based on DAC-YOLO v4 model［J］. Computers and Electronics in Agriculture，2022，202：107418.
［14］Afzaal U，Bhattarai B，Pandeya Y R，et al. An instance segmentation model for strawberry diseases based on mask R-CNN［J］. Sensors，2021，21(19)：6565.
［15］Wang A，Chen H，Liu L，et al. YOLO v10：real-time end-to-end object detection ［EB/OL］. (2024-05-23) ［2024-09-15］. https：//arxiv.org/pdf/2405.14458v1.
［16］Wang C Y，Bochkovskiy A，Liao H M. YOLO v7：trainable bag-of-freebies sets new state-of-the-art for real-time object detectors［C］//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver，BC，Canada.IEEE，2023：7464-7475.
［17］Liu S，Qi L，Qin H F，et al. Path aggregation network for instance segmentation［C］//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City，UT，USA.IEEE，2018：8759-8768.
［18］Lin T Y，Dollár P，Girshick R，et al. Feature pyramid networks for object detection［C］//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu，HI，USA.IEEE，2017：936-944.
［19］Ge Z. Yolox：exceeding yolo series in 2021 ［EB/OL］. (2021-07-18)［2024-09-15］. https：//arxiv.org/abs/2107.08430v1.
［20］Chen Y F，Zhang C Y，Chen B，et al. Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding diagnosis of blood diseases［J］. Computers in Biology and Medicine，2024，170：107917.
［21］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). Nashville，TN，USA.IEEE，2021：13708-13717.
［22］Tian Z，Shen C，Chen H，et al. FCOS：fully convolutional one-stage object detection［EB/OL］. (2019-08-20) ［2024-09-15］. https：//arxiv.org/abs/1904.01355v5.
［23］Zhu X Z，Hu H，Lin S，et al. Deformable ConvNets v2：more deformable，better results［C］//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach，CA，USA.IEEE，2019：9308-9316.
［24］Zheng Z H，Wang P，Liu W，et al. Distance-IoU loss：faster and [JP4]better learning for bounding box regression［J］. Proceedings of the AAAI Conference on Artificial Intelligence，2020，34(7)：12993-13000.
［25］Tong Z，Chen Y，Xu Z，et al. Wise-IoU：bounding box regression loss with dynamic focusing mechanism ［EB/OL］. (2023-04-08) ［2024-09-15］. https：//arxiv.org/abs/2301.10051v.
［26］Wang C Y，Yeh I H，Mark Liao H Y. YOLO v9：learning what you want to learn using programmable gradient information ［EB/OL］. (2024-02-29) ［2024-09-15］. https：//arxiv.org/abs/240.13616v.
［27］Ren S Q，He K M，Girshick R，et al. Faster R-CNN：towards real-time object detection with region proposal networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence，2016，39(6)：1137-1149.

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备注/Memo

备注/Memo:
收稿日期：2024-10-03
基金项目：国家自然科学基金(编号：62077018、62276032)；湖北省教育厅科学技术研究项目(编号：B2021052)。
作者简介：王少芳(1985—)，女，湖北荆州人，博士，讲师，主要从事人工智能、深度学习、分数阶忆阻神经网络研究。E-mail：wangshaofang2022@yangtzeu.edu.cn。
更新日期/Last Update: 2025-10-20