Pear Flower Cluster Quantification Using RGB Drone Imagery

Abstract

High quality fruit production requires the regulation of the crop load on fruit trees by reducing the number of flowers and fruitlets early in the growing season, if the bearing is too high. Several automated flower cluster quantification methods based on proximal and remote imagery methods have been proposed to estimate flower cluster numbers, but their overall performance is still far from satisfactory. For other methods, the performance of the method to estimate flower clusters within a tree is unknown since they were only tested on images from one perspective. One of the main reported bottlenecks is the presence of occluded flowers due to limitations of the top-view perspective of the platform-sensor combinations. In order to tackle this problem, the multi-view perspective from the Red−Green−Blue (RGB) colored dense point clouds retrieved from drone imagery are compared and evaluated against the field-based flower cluster number per tree. Experimental results obtained on a dataset of two pear tree orchards (N = 144) demonstrate that our 3D object-based method, a combination of pixel-based classification with the stochastic gradient boosting algorithm and density-based clustering (DBSCAN), significantly outperforms the state-of-the-art in flower cluster estimations from the 2D top-view (R2 = 0.53), with R2 > 0.7 and RRMSE < 15%.

Keywords

• pear orchards
• flower clusters
• remote sensing
• drones
• rpas
• classification
• clustering
• colored dense point clouds
• rgb