Three dimensional apple tree organs classification and yield estimation algorithm based on multi-features fusion and support vector machine

Abstract

The automatic classification of apple tree organs is of great significance for automatic pruning of apple trees, automatic picking of apple fruits, and estimation of fruit yield. However, there are some problems of dense foliage, partial occlusion and clustering of apple fruits. All of the problems above would contribute to the difficulties of organs classification and yield estimation of the apple trees. In this paper a method based on Color and Shape Multi-features Fusion and Support Vector Machine (SVM) for 3D apple tree organs classification and yield estimation was proposed. The method was designed for dwarf and densely planted apple trees at the early and late maturity stages. 196-dimensional feature vectors composed with Red Green Blue (RGB), Hue Saturation Value (HSV), Curvatures, Fast Point Feature Histogram (FPFH), and Spin Image were extracted firstly. And then the SVM based on linear kernel function was trained, after that the trained SVM was used for apple tree organs classification. Then the position weighted smoothing algorithm was used for classified apple tree organs smoothing. Then the agglomerative hierarchical clustering algorithm was used to recognize single apple fruit for yield estimation. On the same training and test set the experimental results showed that the SVM based on linear kernel function outperformed the KNN algorithm and Ensemble algorithm. The Recall, Precision and F1 score of the proposed method for yield estimation were 93.75%, 96.15% and 94.93% respectively. In summary, to solve the problems of apple tree organs classification and yield estimation in natural apple orchard, a novelty method based on multi-features fusion and SVM was proposed and achieve good performance. Moreover, the proposed method could provide technical support for automatic apple picking, automatic pruning of fruit trees, and automatic information acquisition and management in orchards.