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基于深度学习的蔬菜田精准除草作业区域检测方法

李卫丽 金小俊 于佳琳 陈勇

李卫丽,金小俊,于佳琳,等. 基于深度学习的蔬菜田精准除草作业区域检测方法 [J]. 福建农业学报,2024,39(2):199−205 doi: 10.19303/j.issn.1008-0384.2024.02.010
引用本文: 李卫丽,金小俊,于佳琳,等. 基于深度学习的蔬菜田精准除草作业区域检测方法 [J]. 福建农业学报,2024,39(2):199−205 doi: 10.19303/j.issn.1008-0384.2024.02.010
LI W L, JIN X J, YU J L, et al. Deep Learning Detection of Weeds in Vegetable Fields [J]. Fujian Journal of Agricultural Sciences,2024,39(2):199−205 doi: 10.19303/j.issn.1008-0384.2024.02.010
Citation: LI W L, JIN X J, YU J L, et al. Deep Learning Detection of Weeds in Vegetable Fields [J]. Fujian Journal of Agricultural Sciences,2024,39(2):199−205 doi: 10.19303/j.issn.1008-0384.2024.02.010

基于深度学习的蔬菜田精准除草作业区域检测方法

doi: 10.19303/j.issn.1008-0384.2024.02.010
基金项目: 国家自然科学基金项目(32072498);江苏省研究生科研与实践创新计划项目(KYCX22_1051)
详细信息
    作者简介:

    李卫丽(1985 — ),女,硕士,讲师,主要从事智慧农业、智能机器人技术研究,E-mail:lwl2009seu@126.com

    通讯作者:

    陈勇(1965 — ),男,教授,博士生导师,主要从事机电一体化研究,E-mail:chenyongjsnj@163.com

  • 中图分类号: TP391.41

Deep Learning Detection of Weeds in Vegetable Fields

  • 摘要:   目的  蔬菜生长随机,杂草种类众多。传统杂草识别算法复杂,且仅识别出杂草,未能精准确定除草作业区域。本研究以蔬菜及其伴生杂草为研究对象,拟探索一种基于深度学习的杂草识别与精准除草作业区域检测方法。  方法  通过将原图切分网格图像,利用深度学习模型识别蔬菜、杂草及土壤,将包含杂草的网格图像标记为除草作业区域。选取ShuffleNet、DenseNet和ResNet模型开展识别试验,并采用精度、召回率、F1值和总体准确率、平均准确率分别对验证集和测试集进行评价分析。  结果  所选的3种网络模型均能较好地识别杂草和蔬菜,其中ShuffleNet为杂草识别最优模型,其对杂草的识别具有较为均衡的精度和召回率,分别为95.5%、97%,且其识别速度也达最优,为68.37 fps,能够应用于实时杂草识别。  结论  本研究提出的除草作业区域检测方法具有高度的可行性和极佳的识别效果,可用于蔬菜田间杂草的精准防除。
  • 图  1  原图切分网格图像

    Figure  1.  Images of cropped grids

    图  2  精准除草作业区域检测流程

    Figure  2.  Flowchart of precision determination on weeding areas

    图  3  不同深度学习模型的混淆矩阵图

    Figure  3.  Confusion matrices of deep learning models

    图  4  精准作业区域检测结果

    Figure  4.  Precision determination of weeding areas

    图  5  被识别错误的图片

    Figure  5.  Incorrectly classified images

    表  1  深度学习数据集

    Table  1.   Dataset of deep learning models

    样本类别
    Sample category
    正样本
    Positive sample
    负样本
    Negative sample
    青菜
    Vegetable
    杂草
    Weed
    土壤
    Soil
    训练集
    Training dataset
    3000 3000 3000
    验证集
    Validation dataset
    500 500 500
    测试集
    Testing dataset
    500 500 500
    下载: 导出CSV

    表  2  不同模型的默认超参数

    Table  2.   Hyper-parameters for training convolutional neural networks

    模型
    Neural network
    批尺寸
    Batch size
    初始学习率
    Initial learning rate
    学习率调整策略
    Learning rate policy
    优化器
    Optimizer
    训练周期
    Training epochs
    ShuffleNet 16 0.001 LambdaLR SGD 24
    DenseNet 16 0.001 LambdaLR SGD 24
    ResNet 16 0.0001 StepLR Adam 24
    下载: 导出CSV

    表  3  不同深度学习模型验证集识别结果

    Table  3.   Evaluation metrics on validation dataset obtained by deep learning models

    模型
    Neural Network
    类别
    Category
    精度
    Ppre
    召回率
    Prec
    F1
    F1 score
    ShuffleNet土壤 Soil0.9780.9460.967
    青菜 Vegetable0.9900.9880.989
    杂草 Weed0.9550.9700.962
    DenseNet土壤 Soil0.9720.9740.973
    青菜 Vegetable0.9750.9940.984
    杂草 Weed0.9690.9480.958
    ResNet土壤 Soil0.9810.9460.963
    青菜 Vegetable0.9690.9920.980
    杂草 Weed0.9450.9560.950
    下载: 导出CSV

    表  4  不同深度学习模型测试集评价数据

    Table  4.   Evaluation metrics on test dataset obtained by deep learning models

    模型
    Neural network
    类别
    Category
    总体
    准确率
    OAcc
    平均
    准确率
    AAcc
    网格图像
    识别
    速度
    Speed of
    grid cells/fps
    原图识别
    速度
    Speed of
    full images/fps
    ShuffleNet土壤 Soil0.9670.951207.4568.37
    青菜 Vegetable0.978
    杂草 Weed0.957
    DenseNet土壤 Soil0.9670.949104.0558.94
    青菜 Vegetable0.979
    杂草 Weed0.953
    ResNet土壤 Soil0.9620.941289.5785.42
    青菜 Vegetable0.977
    杂草 Weed0.944
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-07-10
  • 修回日期:  2023-10-05
  • 网络出版日期:  2024-03-28
  • 刊出日期:  2024-02-28

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