UAV Photogrammetry-Based Apple Orchard Blossom Density Estimation and Mapping

Thinning is an important routine for apple growers to manage crop load and improve fruit quality, which can be accomplished through manual, chemical, or mechanical manipulation of flowers fruitlets. Traditionally, blossom thinning relies on human experts’ visual evaluation the flower load, a leading indicator imprecise prone errors. This study aimed develop density mapping algorithm utilizing point clouds reconstructed unmanned aerial vehicle (UAV)-based red-green-blue (RGB) imagery photogrammetry. The was based grid average downsampling white color thresholding, it able generate top-view maps user-defined tree height regions. A preliminary field experiment carried out evaluate algorithm’s accuracy using manual counts row sections as ground truths, coefficient determination (R2) 0.85, root mean square error (RMSE) 1307, normalized RMSE (NRMSE) 9.02% were achieved. utilized monitor blooming rows demonstrated effectively show variations between different dates. results suggested potential UAVs convenient tool assist precise in orchards, while future research should further investigate reliability photogrammetry techniques under image qualities flight settings well influence distribution accuracy.