Niu, Haoyu (UC, Merced), Wang, Dong (USDA ARS Parlier), Ehsani, Reza (University of Florida), Chen, YangQuan (University of California, Merced)

Tree-Level Yield Estimation Using UAV-Based Vegetation Indices and Plant Physiology-Informed Machine Learning

Scheduled for presentation during the Regular Session "UAS Applications IV" (FrC5), Friday, June 24, 2022, 16:20−16:40, Elafiti

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on April 20, 2024

Abstract

Estimating the yield of trees is important to improve orchard management and production. Usually, farmers need to estimate the yield of trees at the early growing stage for field management. However, methods to predict the yield at the individual tree level are currently not available because of the complexity and variability of each tree. Thus, in this article, the authors evaluated the performance of an unmanned aerial vehicle (UAV)-based remote sensing system and machine learning (ML) approaches for yield estimation. A multispectral camera was mounted on the UAV platform to acquire high-resolution images. Eight features were extracted from the UAV imagery, including normalized difference vegetation index (NDVI), green normalized vegetation index (GNDVI), red-edge normalized difference vegetation index (NDVIre), red-edge triangulated vegetation index (RTVIcore), individual tree canopy size, the modified triangular vegetation index (MTVI2), the chlorophyll index-green (CIg), and the chlorophyll index-rededge (CIre). Then, plant physiology-informed machine learning (PPIML) algorithms were applied with the extracted features to predict the yield at the individual tree level. Results showed that the decision tree classifier had the best prediction performance, with an accuracy of 85%.