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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):1−8
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):1−8
LI W L, JIN X J, YU J L, et al. A Deep Learning-based Method for Detection of Precision Weeding Regions in Vegetables [J]. Fujian Journal of Agricultural Sciences,2024,39(2):1−8
Citation: LI W L, JIN X J, YU J L, et al. A Deep Learning-based Method for Detection of Precision Weeding Regions in Vegetables [J]. Fujian Journal of Agricultural Sciences,2024,39(2):1−8

基于深度学习的蔬菜田精准除草作业区域检测方法

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

A Deep Learning-based Method for Detection of Precision Weeding Regions in Vegetables

  • 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确度和召回率,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 sliced by grid

    图  2  精准除草作业区域检测流程图

    Figure  2.  The flow chat of precise weeding area detection

    图  3  不同深度学习模型的混淆矩阵图

    Figure  3.  The confusion matrices of different deep learning models

    图  4  精准作业区域检测结果

    Figure  4.  The result of precise weeding area

    图  5  被识别错误的图片

    Figure  5.  Images that has been wrongly classified

    表  1  深度学习数据集

    Table  1.   The 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 of deep learning models in validation dataset

    模型
    Neural Network
    类别
    Category
    精度
    Precision
    召回率
    Recall
    F1
    F1 score
    ShuffleNet 土壤 Soil 0.978 0.946 0.967
    青菜 Vegetable 0.99 0.988 0.989
    杂草 Weed 0.955 0.97 0.962
    DenseNet 土壤 Soil 0.972 0.974 0.973
    青菜 Vegetable 0.975 0.994 0.984
    杂草 Weed 0.969 0.948 0.958
    ResNet 土壤 Soil 0.981 0.946 0.963
    青菜 Vegetable 0.969 0.992 0.98
    杂草 Weed 0.945 0.956 0.95
    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 of different deep learning models in testing dataset

    模型
    Neural network
    类别
    Category
    总体
    准确率
    Overall
    accuracy
    平均
    准确率
    Average
    accuracy
    网格图像
    识别
    速度fps
    Fps of
    grid cells
    原图识别
    速度
    48/fps
    Fps of
    full images
    ShuffleNet 土壤 Soil 0.967 0.951 207.45 68.37
    青菜 Vegetable 0.978
    杂草 Weed 0.957
    DenseNet 土壤 Soil 0.967 0.949 104.05 58.94
    青菜 Vegetable 0.979
    杂草 Weed 0.953
    ResNet 土壤 Soil 0.962 0.941 289.57 85.42
    青菜 Vegetable 0.977
    杂草 Weed 0.944
    下载: 导出CSV
  • [1] 金月, 肖宏儒, 曹光乔, 等. 我国叶类蔬菜机械化水平现状与评价方法研究 [J]. 中国农机化学报, 2020, 41(12):196−201.

    JIN Y, XIAO H R, CAO G Q, et al. Research on status and evaluation methods of leafy vegetable mechanization production level in China [J]. Journal of Chinese Agricultural Mechanization, 2020, 41(12): 196−201. (in Chinese)
    [2] 中华人民共和国国家统计局. 农业年度数据 [EB/OL]. http://data.stats.gov.cn/easyquery.htm?cn=C01.
    [3] 刘文, 徐丽明, 邢洁洁, 等. 作物株间机械除草技术的研究现状 [J]. 农机化研究, 2017, 39(1):243−250.

    LIU W, XU L M, XING J J, et al. Research status of mechanical intra-row weed control in row crops [J]. Journal of Agricultural Mechanization Research, 2017, 39(1): 243−250. (in Chinese)
    [4] 强胜. 我国杂草学研究现状及其发展策略 [J]. 植物保护, 2010, 36(4):1−5. doi: 10.3969/j.issn.0529-1542.2010.04.001

    QIANG S. Current status and development strategy for weed science in China [J]. Plant Protection, 2010, 36(4): 1−5. (in Chinese) doi: 10.3969/j.issn.0529-1542.2010.04.001
    [5] 陈德润, 王书茂, 王秀. 农田杂草识别技术的现状与展望 [J]. 中国农机化, 2005, 26(2):35−38.

    CHEN D R, WANG S M, WANG X. Status and prospect for recognition technology of farm weeds [J]. Chinese Agriculture Mechanization, 2005, 26(2): 35−38. (in Chinese)
    [6] 何义川, 汤智辉, 李光新, 等. 葡萄园除草技术研究现状与发展趋势 [J]. 中国农机化学报, 2018, 39(9):34−37.

    HE Y C, TANG Z H, LI G X, et al. Research on current status and developing tendency of the vineyard weeding [J]. Journal of Chinese Agricultural Mechanization, 2018, 39(9): 34−37. (in Chinese)
    [7] 李东升, 张莲洁, 盖志武, 等. 国内外除草技术研究现状 [J]. 森林工程, 2002, 18(1):17−18.

    LI D S, ZHANG L J, GAI Z W, et al. Research situations of weeding techniques in abroad and home [J]. Forest Engineering, 2002, 18(1): 17−18. (in Chinese)
    [8] 洪晓玮, 陈勇, 杨超淞, 等. 有机蔬菜大棚除草机器人研制 [J]. 制造业自动化, 2021, 43(5):33−36,71.

    HONG X W, CHEN Y, YANG C S, et al. Development of a weeding robot for organic vegetable greenhouse [J]. Manufacturing Automation, 2021, 43(5): 33−36,71. (in Chinese)
    [9] HASANUZZAMAN M, MOHSIN S M, BHUYAN M H M B, et al. Phytotoxicity, environmental and health hazards of herbicides: Challenges and ways forward[M]//Agrochemicals Detection, Treatment and Remediation. Amsterdam: Elsevier, 2020: 55-99.
    [10] 何荣昌. 浅析农田除草剂对土壤生态环境的影响 [J]. 南方农业, 2019, 13(6):187−188.

    HE R C. Analysis on the influence of herbicide on soil ecological environment in farmland [J]. South China Agriculture, 2019, 13(6): 187−188. (in Chinese)
    [11] 东辉, 陈鑫凯, 孙浩, 等. 基于改进YOLOv4和图像处理的蔬菜田杂草检测 [J]. 图学学报, 2022, 43(4):559−569.

    DONG H, CHEN X K, SUN H, et al. Weed detection in vegetable field based on improved YOLOv4 and image processing [J]. Journal of Graphics, 2022, 43(4): 559−569. (in Chinese)
    [12] 兰天, 李端玲, 张忠海, 等. 智能农业除草机器人研究现状与趋势分析 [J]. 计算机测量与控制, 2021, 29(5):1−7.

    LAN T, LI D L, ZHANG Z H, et al. Analysis on research status and trend of intelligent agricultural weeding robot [J]. Computer Measurement & Control, 2021, 29(5): 1−7. (in Chinese)
    [13] 马娟, 董金皋. 微生物除草剂与生物安全 [J]. 植物保护, 2006, 32(1):9−12.

    MA J, DONG J G. Microbial herbicides and biosafety [J]. Plant Protection, 2006, 32(1): 9−12. (in Chinese)
    [14] 金小俊, 孙艳霞, 陈勇, 等. 基于深度学习的草坪杂草识别与除草剂喷施区域检测方法 [J]. 草地学报, 2022, 30(6):1543−1549.

    JIN X J, SUN Y X, CHEN Y, et al. Weed recognition and herbicide spraying area detection in turf based on deep learning [J]. Acta Agrestia Sinica, 2022, 30(6): 1543−1549. (in Chinese)
    [15] 孙艳霞, 陈燕飞, 金小俊, 等. 基于人工智能的青菜幼苗与杂草识别方法 [J]. 福建农业学报, 2021, 36(12):1484−1490.

    SUN Y X, CHEN Y F, JIN X J, et al. AI differentiation of Bok choy seedlings from weeds [J]. Fujian Journal of Agricultural Sciences, 2021, 36(12): 1484−1490. (in Chinese)
    [16] 朱伟兴, 金飞剑, 谈蓉蓉. 基于颜色特征与多层同质性分割算法的麦田杂草识别[J]. 农业机械学报, 2007, 38(12): 120−124.

    ZHU W X, JIN F J, TAN R R. Weed recognition method based on color feature and hierarchical homogeneity segmentation in wheat field[J]. Transactions of the Chinese Society for Agricultural Machinery, 2007, 38(12): 120−124. (in Chinese)
    [17] BAKHSHIPOUR A, JAFARI A, NASSIRI S M, et al. Weed segmentation using texture features extracted from wavelet sub-images [J]. Biosystems Engineering, 2017, 157: 1−12. doi: 10.1016/j.biosystemseng.2017.02.002
    [18] 杨涛, 李晓晓. 机器视觉技术在现代农业生产中的研究进展 [J]. 中国农机化学报, 2021, 42(3):171−181.

    YANG T, LI X X. Research progress of machine vision technology in modern agricultural production [J]. Journal of Chinese Agricultural Mechanization, 2021, 42(3): 171−181. (in Chinese)
    [19] 赵娜, 赵平, 高轶军. 机器视觉技术在我国现代农业生产中的应用研究 [J]. 天津农学院学报, 2015, 22(2):55−58.

    ZHAO N, ZHAO P, GAO Y J. Study on application of machine vision technology to modern agriculture in China [J]. Journal of Tianjin Agricultural University, 2015, 22(2): 55−58. (in Chinese)
    [20] 刘现, 郑回勇, 施能强, 等. 人工智能在农业生产中的应用进展 [J]. 福建农业学报, 2013, 28(6):609−614.

    LIU X, ZHENG H Y, SHI N Q, et al. Artificial intelligence in agricultural applications [J]. Fujian Journal of Agricultural Sciences, 2013, 28(6): 609−614. (in Chinese)
    [21] OSORIO K, PUERTO A, PEDRAZA C, et al. A deep learning approach for weed detection in lettuce crops using multispectral images [J]. AgriEngineering, 2020, 2(3): 471−488. doi: 10.3390/agriengineering2030032
    [22] 彭文, 兰玉彬, 岳学军, 等. 基于深度卷积神经网络的水稻田杂草识别研究 [J]. 华南农业大学学报, 2020, 41(6):75−81. doi: 10.7671/j.issn.1001-411X.202007029

    PENG W, LAN Y B, YUE X J, et al. Research on paddy weed recognition based on deep convolutional neural network [J]. Journal of South China Agricultural University, 2020, 41(6): 75−81. (in Chinese) doi: 10.7671/j.issn.1001-411X.202007029
    [23] YU J L, SCHUMANN A W, SHARPE S M, et al. Detection of grassy weeds in bermudagrass with deep convolutional neural networks [J]. Weed Science, 2020, 68(5): 545−552. doi: 10.1017/wsc.2020.46
    [24] YU J L, SHARPE S M, SCHUMANN A W, et al. Deep learning for image-based weed detection in turfgrass [J]. European Journal of Agronomy, 2019, 104: 78−84. doi: 10.1016/j.eja.2019.01.004
    [25] YU J L, SCHUMANN A W, CAO Z, et al. Weed detection in perennial ryegrass with deep learning convolutional neural network [J]. Frontiers in Plant Science, 2019, 10: 1422. doi: 10.3389/fpls.2019.01422
    [26] 金小俊, 孙艳霞, 于佳琳, 等. 基于深度学习与图像处理的蔬菜苗期杂草识别方法 [J]. 吉林大学学报(工学版), 2023, 53(8):2421−2429.

    JIN X J, SUN Y X, YU J L, et al. Weed recognition in vegetable at seedling stage based on deep learning and image processing [J]. Journal of Jilin University:Engineering and Technology Edition, 2023, 53(8): 2421−2429.
    [27] JIN X J, SUN Y X, CHE J, et al. A novel deep learning-based method for detection of weeds in vegetables [J]. Pest Management Science, 2022, 78(5): 1861−1869. doi: 10.1002/ps.6804
    [28] JIN X J, CHE J, CHEN Y. Weed identification using deep learning and image processing in vegetable plantation [J]. IEEE Access, 2021, 9: 10940−10950. doi: 10.1109/ACCESS.2021.3050296
    [29] 毛文华, 姜红花, 胡小安, 等. 基于位置特征的行间杂草识别方法 [J]. 农业机械学报, 2007, 38(11):74−76,83. doi: 10.3969/j.issn.1000-1298.2007.11.018

    MAO W H, JIANG H H, HU X A, et al. Between-row weed detection method based on position feature [J]. Transactions of the Chinese Society for Agricultural Machinery, 2007, 38(11): 74−76,83. (in Chinese) doi: 10.3969/j.issn.1000-1298.2007.11.018
    [30] PYTORCH. Tensors and dynamic neural networks in python with strong GPU acceleration. [DB/OL]. (2020-01-18)[2020-03-05]. https://github.com/pytorch/pytorch.
    [31] 舒娜, 刘波, 林伟伟, 等. 分布式机器学习平台与算法综述 [J]. 计算机科学, 2019, 46(3):9−18. doi: 10.11896/j.issn.1002-137X.2019.03.002

    SHU N, LIU B, LIN W W, et al. Survey of distributed machine learning platforms and algorithms [J]. Computer Science, 2019, 46(3): 9−18. (in Chinese) doi: 10.11896/j.issn.1002-137X.2019.03.002
    [32] 黄海松, 陈星燃, 韩正功, 等. 基于多尺度注意力机制和知识蒸馏的茶叶嫩芽分级方法 [J]. 农业机械学报, 2022, 53(9):399−407,458.

    HUANG H S, CHEN X R, HAN Z G, et al. Tea buds grading method based on multiscale attention mechanism and knowledge distillation [J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(9): 399−407,458. (in Chinese)
    [33] 李子茂, 徐杰, 郑禄, 等. 基于改进DenseNet的茶叶病害小样本识别方法 [J]. 农业工程学报, 2022, 38(10):182−190.

    LI Z M, XU J, ZHENG L, et al. Small sample recognition method of tea disease based on improved DenseNet [J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(10): 182−190. (in Chinese)
    [34] GAO S H, CHENG M M, ZHAO K, et al. Res2Net: A new multi-scale backbone architecture [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(2): 652−662. doi: 10.1109/TPAMI.2019.2938758
    [35] 吕梦棋, 张芮祥, 贾浩, 等. 基于改进ResNet玉米种子分类方法研究 [J]. 中国农机化学报, 2021, 42(4):92−98.

    LÜ M Q, ZHANG R X, JIA H, et al. Research on seed classification based on improved ResNet [J]. Journal of Chinese Agricultural Mechanization, 2021, 42(4): 92−98. (in Chinese)
    [36] HANG X Y, ZHOU X Y, LIN M X, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT. IEEE, 2018: 6848-6856.
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  • 收稿日期:  2023-07-10
  • 修回日期:  2023-10-05
  • 网络出版日期:  2024-03-28

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