SCALE-AWARE POMEGRANATE YIELD PREDICTION USING UAV IMAGERY AND MACHINE LEARNING.

Accurately estimating the yield is one of the most important research projects for orchard management. Growers need to estimate the yield of trees at an early stage to make smart decisions 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. To improve the accuracy of yield prediction, the authors evaluate the performance of an unmanned aerial vehicle (UAV)-based remote sensing system and machine learning (ML) approaches for tree-level pomegranate yield estimation. A lightweight multispectral camera was mounted on the UAV platform to acquire high-resolution images. Eight features were extracted from the UAV images, including the normalized difference vegetation index (NDVI), the green normalized vegetation index (GNDVI), the 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). Correlation coefficients (R2 ) were calculated between these vegetation indices and tree yield. Classic machine learning approaches 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%. The study demonstrated the potential of using UAV-based remote sensing methods, coupled with ML algorithms, for pomegranate yield estimation. Predicting the yield at the individual tree level will enable the stakeholders to manage the orchard at different scales, thus improving field management efficiency. [ABSTRACT FROM AUTHOR]