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基于改进密度峰值聚类算法的梨花密度分级

周桂红, 孙乐琳, 梁芳芳, 张秀花

周桂红, 孙乐琳, 梁芳芳, 张秀花. 基于改进密度峰值聚类算法的梨花密度分级[J]. 农业工程学报, 2023, 39(1): 126-135. DOI: 10.11975/j.issn.1002-6819.202207204
引用本文: 周桂红, 孙乐琳, 梁芳芳, 张秀花. 基于改进密度峰值聚类算法的梨花密度分级[J]. 农业工程学报, 2023, 39(1): 126-135. DOI: 10.11975/j.issn.1002-6819.202207204
ZHOU Guihong, SUN Lelin, LIANG Fangfang, ZHANG Xiuhua. Pear flower density classification based on improved density peak clustering algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(1): 126-135. DOI: 10.11975/j.issn.1002-6819.202207204
Citation: ZHOU Guihong, SUN Lelin, LIANG Fangfang, ZHANG Xiuhua. Pear flower density classification based on improved density peak clustering algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(1): 126-135. DOI: 10.11975/j.issn.1002-6819.202207204

基于改进密度峰值聚类算法的梨花密度分级

基金项目: 国家自然科学基金(62106065);河北省现代农业产业技术体系梨创新团队现代果园装备与智能化(HBCT2021210206)

Pear flower density classification based on improved density peak clustering algorithm

  • 摘要: 精准判断梨花疏密程度是自动疏花的基础。为了更好地判断梨花密度,该研究提出了基于改进密度峰值聚类算法的梨花密度分级方法。该方法首先提取梨花位置坐标,获取需要聚类的数据点。其次,为了实现梨花图像的密度分级,针对原有密度峰值聚类算法在梨花密度分级中的不足,结合梨花密度分级需求,改进了对聚类中心的选取方式,通过4组局部密度和中心偏移距离分割阈值将决策图划分为4部分来选取聚类中心,分别对应高、中、低密度以及无需疏花处理等4个等级,实现了对疏密合理的梨花图像的准确分级。最后,针对只有团状分布、稀疏分布及大尺度特写的梨花分布聚类分级不准确的问题,改进了两点间的距离dij参数的计算方法,统一梨花尺度大小和密度分级标准,对所有分布类型的梨花图像均能实现合理的密度分级。试验结果表明,该研究算法能够适应不同尺度大小的梨花图像,预测准确率为94.89%,密度分级准确率达到94.29%,可实现自然环境下局部花簇的密度分级,为机器智能疏花提供了技术支持。
    Abstract: It is important to judge the density of pear flower accurately for automatic thinning. In order to judge the density of pear flowers precisely and achieve the purpose of automatic flower thinning, a density classification of pear flower images method based on improved density peak clustering algorithm was proposed in this study. The pear flower image samples used in the experiment were taken from the research base of Hebei Agricultural University, Niugang Village, Yi County, Baoding City, Hebei Province, and the variety was three-year-old Qiuyue. In this study, the images of pear flowers in natural environment were collected on April 18, 2021, when the pear flowers were in full bloom. The pear flower recognition images with high detection accuracy were obtained by using the depth learning model to detect the pear flower. The central coordinates of the target detection frame were extracted from the recognition images to obtain the data points that needed to be clustered. Secondly, in order to achieve the density classification of pear flower images, the method of selecting the cluster center was improved according to the shortcomings of the original density peak clustering algorithm in the density classification of pear flower and the requirements of pear flower density classification. The decision graph was divided into four parts by four groups of local density and center offset distance segmentation thresholds to select the cluster centers. These four parts corresponded to four grades of high, medium and low density and no thinning treatment. The accurate classification of pear flower images with reasonable density was realized. Finally, to solve the problem of inaccurate clustering classification of pear flower distribution with only cluster distribution, sparse distribution and large-scale close-up, the calculation method of dij parameter of distance between two points was improved. The pear flower scale and density grading standard were unified, and reasonable density grading could be achieved for all distribution types of pear flower images. The experimental results showed that the proposed algorithm could adapt to pear flower images of different scales. The accuracy of scale prediction was 94.89%, and the accuracy of density classification was 94.29%. Compared with the existing methods, the proposed method could achieve the density classification of local flower clusters in natural environment. the algorithm in this paper was compared with K-means algorithm and DBSCAN algorithm, and the accuracy of density classification was 94.29% and 68.57% respectively. Although the density classification accuracy of K-Means clustering algorithm was high, it lacked the algorithm steps to automatically select the clustering centers of different density classification, and was too sensitive to noise points. The density calculation method of DBSCAN algorithm lacked continuity, and the division of density grade was not precise enough. Based on the comprehensive analysis of density classification accuracy and cluster center selection method, the improved density peak clustering algorithm in this paper had the best effect. The clustering centers of different density grade could be automatically selected and the error of random initialization of clustering centers could be reduced. The proposed algorithm was robust to noise points and could eliminate noise points effectively. The proposed algorithm adopted soft statistics method for density calculation, which had continuity and was more accurate for density classification. It provides technical support for analyzing the density of pear flower and machine intelligent flower thinning.
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    1. 司永胜,孔德浩,王克俭,刘丽星,杨欣. 基于改进YOLOv8-Seg的苹果单枝条花序疏除方法. 农业工程学报. 2024(14): 100-108 . 本站查看

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出版历程
  • 收稿日期:  2022-07-20
  • 修回日期:  2022-12-06
  • 发布日期:  2023-01-14

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