Apple orchard flower clusters density mapping by unmanned aerial vehicle RGB acquisitions

Flower load is one of the earlier indicators of potential yield in fruit orchards. Usually, a higher flower clusters number are present on a tree than needed for an optimal production. The most used techniques to manage the flower load are manual, mechanical, and chemical thinning. The main issue is to calibrate these techniques on the base of the desired yield. Drone imagery, being able to collect highly detailed information, could offer a solution to automate flower counting since manual flower counting would be too laborious. The main goals of this study were to test an easy to use and quick be analyzed data acquisition for sudden field interventions, short computing time and reliability. This was achieved by applying and comparing two methodologies that allow to map apple's flower clusters density at full bloom stage by processing Unmanned aerial Vehicle's (UAV) imagery with binary classification and K-Nearest Neighbor algorithm, respectively. A comparison between the flower cluster estimation and the actual cluster load analysis highlighted that mapping flower clusters by binary classification is more suitable than machine learning in terms of image processing because it allowed to have a quicker (8 minutes) , easier and less noise - affected image analysis with R2 values ranging from 0,60 to 0,71. Furthermore, the proposed methodology seemed to be able to manage with a high spatial variability since it produced a map that clearly corresponded with zonal field conditions.