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基于改进模糊C均值聚类算法的草坪杂草识别

化春键, 张爱榕, 蒋毅, 俞建峰, 陈莹

化春键, 张爱榕, 蒋毅, 等. 基于改进模糊C均值聚类算法的草坪杂草识别[J]. 华南农业大学学报, 2022, 43(3): 107-115. DOI: 10.7671/j.issn.1001-411X.202109005
引用本文: 化春键, 张爱榕, 蒋毅, 等. 基于改进模糊C均值聚类算法的草坪杂草识别[J]. 华南农业大学学报, 2022, 43(3): 107-115. DOI: 10.7671/j.issn.1001-411X.202109005
HUA Chunjian, ZHANG Airong, JIANG Yi, et al. Lawn weed recognition based on improved fuzzy C-means clustering algorithm[J]. Journal of South China Agricultural University, 2022, 43(3): 107-115. DOI: 10.7671/j.issn.1001-411X.202109005
Citation: HUA Chunjian, ZHANG Airong, JIANG Yi, et al. Lawn weed recognition based on improved fuzzy C-means clustering algorithm[J]. Journal of South China Agricultural University, 2022, 43(3): 107-115. DOI: 10.7671/j.issn.1001-411X.202109005

基于改进模糊C均值聚类算法的草坪杂草识别

基金项目: 国家自然科学基金(62173160)
详细信息
    作者简介:

    化春键,副教授,博士,主要从事机器视觉与传感器技术研究,E-mail: cjhua@jiangnan.edu.cn

  • 中图分类号: TN911.73

Lawn weed recognition based on improved fuzzy C-means clustering algorithm

  • 摘要:
    目的 

    为了实现草坪杂草管理的精准化施药,针对自然环境中杂草与草坪颜色相近导致杂草难以分割的问题,提出一种改进模糊C均值(Fuzzy C-means, FCM)聚类的分割算法。

    方法 

    利用超绿算子提取感兴趣区域,融合HSV空间的多通道信息进行图像预处理,扩大杂草与草坪的特征差异。使用区域面积约束滤波范围,去除预处理图像中的草坪背景噪声,降低中值滤波造成的目标区域灰度级损失。提出一种各向灰度分布差异(Difference of gray distribution, DGD)检测算子,在聚类过程中引入像素周围不同方向的灰度分布差异特征实现草坪杂草分割。

    结果 

    与传统FCM、FCM-S2、FCMNLS以及RSFCM算法相比,本文算法对大多数噪声区域抑制效果较好,可以实现较为理想的杂草分割效果。本文算法能有效分割草坪杂草,平均分割准确率达到91.45%,比FCM、FCM-S2、FCMNLS和RSFCM算法分别提高16.35%、4.12%、6.80%和8.06%。

    结论 

    本文算法可有效地分割自然环境中的草坪杂草,为草坪杂草精准化施药提供了条件,具有实际应用价值。

    Abstract:
    Objective 

    In order to realize the precise application of herbicides for lawn weed management, an improved fuzzy C-means (FCM) clustering segmentation algorithm was proposed to solve the problem that it is difficult to segment weeds due to the similar color between weeds and lawns in natural environment.

    Method 

    The region of interest was extracted by extra-green operator, and the multi-channel information in HSV space was incorporated for image preprocessing to expand the feature difference between weeds and lawns. The filtering range was constrained using region area to remove the lawn background noise in the preprocessed image and reduce the gray level loss in the target region caused by median filtering. An anisotropic detection operator of difference of gray distribution(DGD) was proposed. In the clustering process, the gray distribution difference characteristics in different directions around pixels were introduced to realize lawn weed segmentation.

    Result 

    Compared with the traditional FCM, FCM-S2, FCMNLS and RSFCM algorithms, the algorithm in this paper (DGDFCM) had better suppression effect on most noise areas and could achieve ideal weed segmentation effect. The DGDFCM algorithm could effectively segment lawn weeds, and the average segmentation accuracy was 91.45%, which was 16.35%, 4.12%, 6.80% and 8.06% higher than FCM, FCM-S2, FCMNLS and RSFCM algorithms, respectively.

    Conclusion 

    The DGDFCM algorithm can effectively segment lawn weeds in natural environment, provides the condition of precise application of herbicides for lawn weeds, and has a practical application value.

  • 图  1   不同生长状态的草坪杂草

    Figure  1.   Lawn weeds in different growth states

    图  2   图像预处理结果

    Figure  2.   The results of preprocessed images

    图  3   不同处理下的中值滤波

    Figure  3.   Median filter under different treatment

    图  4   各向灰度分布差异检测算子

    Figure  4.   The detection operator of gray distribution difference in different direction

    图  5   图像的DGD特征

    Figure  5.   The DGD feature of image

    图  6   本文方法流程图

    Figure  6.   Flow chart of method in this paper

    图  7   不同算法的分割结果 (DGDFCM为本文算法)

    Figure  7.   Segmentation results of different algorithms (DGDFCM is the algorithm in this paper)

    表  1   草坪和杂草样本表型

    Table  1   Phenotypes of lawn and weed samples

    样本编号
    Sample number
    草坪表型
    Lawn phenotype
    杂草表型
    Weed phenotype
    叶片颜色
    Leaf color
    生长状态
    Growth state
    叶片形状
    Leaf shape
    叶片颜色
    Leaf color
    生长状态
    Growth state
    1 青绿 密集 细叶 青绿 丛生
    2 黄绿 密集 大阔叶 黄绿 单株
    3 黄绿 密集 小阔叶 黄绿 单株
    4 青绿 密集 大阔叶 青绿 丛生
    5 嫩绿 密集 细叶 青绿 单株
    6 嫩绿 稀疏 大阔叶 青绿 丛生
    7 青绿 密集 小阔叶 青绿 单株
    8 嫩绿 稀疏 大阔叶 嫩绿 单株
    下载: 导出CSV

    表  2   不同算法的图像分割质量评价

    Table  2   Quality evaluation for image segmentation of different algorithm %

    样本编号 Sample number 指标1) Index FCM FCM-S2 FCMNLS RSFCM DGDFCM
    1 SA 59.07 72.11 54.14 62.81 85.78
    UR 11.83 11.27 11.44 9.34 7.41
    OR 13.23 3.05 2.14 23.43 1.69
    2 SA 95.21 95.74 95.67 96.01 95.87
    UR 2.51 2.55 2.53 2.47 2.51
    OR 1.29 0.85 0.80 1.12 0.88
    3 SA 67.75 86.07 82.99 77.80 87.57
    UR 8.40 8.42 8.43 7.08 7.36
    OR 12.02 2.49 2.46 10.03 2.61
    4 SA 89.21 92.59 92.67 91.71 93.34
    UR 3.86 3.84 3.86 3.71 3.76
    OR 3.69 1.57 1.13 2.72 1.32
    5 SA 79.07 83.79 86.58 83.79 91.61
    UR 9.75 8.17 8.33 5.25 6.13
    OR 9.47 2.78 1.60 10.35 1.39
    6 SA 84.37 93.77 93.98 90.96 94.60
    UR 4.11 3.51 3.53 3.20 3.34
    OR 6.43 1.07 0.71 3.92 0.73
    7 SA 83.81 90.74 90.20 87.84 92.23
    UR 5.88 5.00 5.03 4.66 4.80
    OR 6.08 1.99 1.58 4.50 1.53
    8 SA 70.29 87.84 88.73 86.08 90.60
    UR 8.24 5.91 5.96 4.85 5.33
    OR 12.17 1.21 0.71 7.26 0.19
    平均
    Average
    SA 78.60 87.83 85.62 84.63 91.45
    UR 6.82 6.08 6.14 5.07 5.08
    OR 8.05 1.88 1.39 7.92 1.09
     1) SA: 分割准确率,UR: 欠分割率,OR: 过分割率
     1) SA: Segmentation accuracy, UR: Under segmentation rate, OR: Over segmentation rate
    下载: 导出CSV
  • [1]

    IGNATIEVA M, ERIKSSON F, ERIKSSON T, et al. The lawn as a social and cultural phenomenon in Sweden[J]. Urban Forestry & Urban Greening, 2017, 21: 213-223.

    [2] 杭楠, 王翔宇, 张蕴薇, 等. 结缕草草坪杂草化学防除策略[J]. 草业科学, 2019, 36(9): 2259-2269.
    [3]

    HOCKEMEYER K, KOCH P L. Alternative and low‐use‐rate herbicides offer similar levels of weed control to current standards in turfgrass lawns in the upper midwest[J]. Crop, Forage & Turfgrass Management, 2019, 5(1): 1-6.

    [4]

    MARTELLONI L, FONTANELLI M, CATUREGLI L, et al. Flaming to control weeds in seashore paspalum (Paspalum vaginatum Sw. ) turfgrass[J]. Journal of Agricultural Engineering, 2019, 50(3): 105-112. doi: 10.4081/jae.2019.904

    [5]

    JIN X, 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

    [6]

    YU J, 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): 1-31. doi: 10.1017/wsc.2020.76

    [7]

    WATCHAREERUETAI U, MATSUMOTO Y T T, KUDO H, et al. Computer vision based methods for detecting weeds in lawns[J]. Machine Vision & Applications, 2006, 17(5): 287-296.

    [8]

    PARRA L, MARIN J, YOUSFI S, et al. Edge detection for weed recognition in lawns[J]. Computers and Electronics in Agriculture, 2020, 176(2): 105684.

    [9]

    TONGBRAM S, SHIMRAY B A, SINGH L S, et al. A novel image segmentation approach using fcm and whale optimization algorithm[J]. Journal of Ambient Intelligence and Humanized Computing, 2021(1): 1-15.

    [10] 兰蓉, 林洋. 抑制式非局部空间直觉模糊C−均值图像分割算法[J]. 电子与信息学报, 2019, 41(6): 1472-1479.
    [11] 赵泉华, 王春畅, 李玉. 基于混合邻域约束项的改进FCM算法[J]. 控制与决策, 2021, 36(6): 1457-1464.
    [12] 张春龙, 张楫, 张俊雄, 等. 近色背景中树上绿色苹果识别方法[J]. 农业机械学报, 2014, 45(10): 277-281. doi: 10.6041/j.issn.1000-1298.2014.10.043
    [13] 张田, 田勇, 王子, 等. 基于清晰度评价的自适应阈值图像分割法[J]. 东北大学学报(自然科学版), 2020, 41(9): 1231-1238. doi: 10.12068/j.issn.1005-3026.2020.09.003
    [14] 王宁, 殷长春, 高玲琦, 等. 基于曲波变换的航空电磁数据去噪方法研究[J]. 地球物理学报, 2020, 63(12): 4592-4603. doi: 10.6038/cjg2020N0365
    [15]

    CAI W, CHEN S, ZHANG D. Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation[J]. Pattern Recognition, 2007, 40(3): 825-838. doi: 10.1016/j.patcog.2006.07.011

    [16] 毛林, 赵利强, 于明安, 等. 基于图像局部熵的混合水平集模型甲状旁腺分割[J]. 光学学报, 2019, 39(12): 256-264.
    [17] 徐金东, 赵甜雨, 冯国政, 等. 基于上下文模糊C均值聚类的图像分割算法[J]. 电子与信息学报, 2021, 43(7): 2079-2086. doi: 10.11999/JEIT200263
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出版历程
  • 收稿日期:  2021-09-03
  • 网络出版日期:  2023-05-17
  • 刊出日期:  2022-05-09

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