A method for organs classification and fruit counting on pomegranate trees based on multi-features fusion and support vector machine by 3D point cloud

人工智能 模式识别(心理学) 支持向量机 聚类分析 RGB颜色模型 点云 数据库扫描 数学 分类器(UML) 平滑的 计算机科学 层次聚类 计算机视觉 模糊聚类 树冠聚类算法
作者
Chunlong Zhang,Kaifei Zhang,Luzhen Ge,Kunlin Zou,Song Wang,Junxiong Zhang,Wei Li
出处
期刊:Scientia Horticulturae [Elsevier BV]
卷期号:278: 109791-109791 被引量:27
标识
DOI:10.1016/j.scienta.2020.109791
摘要

Organs classification and fruit counting on pomegranate trees are of great significance for horticulture works and robotic picking. However, there are still some challenges: (1) illumination is uncontrollable in the natural environment; (2) traditional 2D image-based methods for classification and recognition are limited by occlusion on pomegranate trees. In this paper, a method for organs classification and fruit counting on pomegranate trees based on multi-features fusion and Support Vector Machine (SVM) was proposed. It was constructed by the following steps: (1) Three-dimensional point clouds of pomegranate trees were obtained by an RGB-D camera; (2) Three-dimensional point clouds were preprocessed; (3) Color and shape features were extracted to train the SVM classifier; (4) The obtained classifier model was used for organs classification on pomegranate trees; (5) A K-nearest neighbor (KNN) smoothing based on weighted Euclidean distance was used to improve the accuracy of classification; (6) An agglomerative-divisive hierarchical clustering was used to count pomegranate fruit. The experiment results showed that the SVM classifier based on color and shape feature had an accuracy of 0.75 for fruit and 0.99 for non-fruit. The fruit counting based on agglomerative-divisive hierarchical clustering had a recall of 87.74 % and a precision of 78.15 %. Compared with density-based spatial clustering of applications with noise (DBSCAN), the recall has improved significantly. This method was aimed at the whole fruit tree, so it has advantages in the completeness of information. The results indicated that the proposed method was effective and feasible for organs classification and yield estimation on pomegranate trees in the natural environment.
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