Inversion of Leaf Area Index in Citrus Trees Based on Multi-Modal Data Fusion from UAV Platform

Abstract

The leaf area index (LAI) is an important growth indicator used to assess the health status and growth of citrus trees. Although LAI estimation based on unmanned aerial vehicle (UAV) platforms has been widely used for field crops, mainly focusing on food crops, less research has been reported on the application to fruit trees, especially citrus trees. In addition, most studies have used single-modal data for modeling, but some studies have shown that multi-modal data can be effective in improving experimental results. This study utilizes data collected from a UAV platform, including RGB images and point cloud data, to construct single-modal regression models named VoVNet (using RGB data) and PCNet (using point cloud data), as well as a multi-modal regression model called VPNet (using both RGB data and point cloud data). The LAI of citrus trees was estimated using deep neural networks, and the results of two experimental hyperparameters (loss function and learning rate) were compared under different parameters. The results of the study showed that VoVNet had Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-Squared (R2) of 0.129, 0.028, and 0.647, respectively. In comparison, PCNet decreased by 0.051 and 0.014 to 0.078 and 0.014 for MAE and MSE, respectively, while R2 increased by 0.168 to 0.815. VPNet decreased by 0% and 42.9% relative to PCNet in terms of MAE and MSE to 0.078 and 0.008, respectively, while R2 increased by 5.6% to 0.861. In addition, the use of loss function L1 gave better results than L2, while a lower learning rate gave better results. It is concluded that the fusion of RGB data and point cloud data collected by the UAV platform for LAI estimation is capable of monitoring citrus trees’ growth process, which can help farmers to track the growth condition of citrus trees and improve the efficiency and quality of orchard management.