LAUSR

Abstract 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.