Feasibility Study on the Classification of Persimmon Trees’ Components Based on Hyperspectral LiDAR

Abstract

Intelligent management of trees is essential for precise production management in orchards. Extracting components' information from individual fruit trees is critical for analyzing and understanding their general growth. This study proposes a method to classify persimmon tree components based on hyperspectral LiDAR data. We extracted nine spectral feature parameters from the colorful point cloud data and performed preliminary classification using random forest, support vector machine, and backpropagation neural network methods. However, the misclassification of edge points with spectral information reduced the accuracy of the classification. To address this, we introduced a reprogramming strategy by fusing spatial constraints with spectral information, which increased the overall classification accuracy by 6.55%. We completed a 3D reconstruction of classification results in spatial coordinates. The proposed method is sensitive to edge points and shows excellent performance for classifying persimmon tree components.

Code and data availability

The paper describes HSL point cloud data of persimmon/lemon tree samples and classification code, but provides no public dataset or code deposit. The Data Availability Statement says only 'Not applicable,' and no author URL or repository is given.

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