Using unmanned aerial vehicle acquired RGB images and Density-Cluster-Count model for tree-level apple flower quantification

Abstract

Accurate estimation of apple flower quantity is vital for flower thinning strategies, yield prediction, and other aspects related to orchard management. Compared to flower quantity estimation based on partial regions, achieving tree-level flower quantification is more meaningful. To achieve tree-level apple flower cluster counting, an aerial apple tree image-based approach was developed based on density estimation and density peak. Initially, images of apple trees captured by UAV were input into the apple flower density estimation model, which was based on a multi-scale feature fusion network, to obtain a density map of apple flowers. Utilizing the growth patterns and distribution characteristics of apple flowers and clusters, apple flower cluster counting was accomplished through the identification of local density peaks. Following testing, the proposed method achieved a Mean Absolute Error (MAE) of 5.39 and a Root Mean Squared Error (RMSE) of 8.10 in apple flower cluster counting. The MAE was decreased by 3.83, 16.22, and 11.13 compared to apple flower cluster counting methods based on YOLOv5s, U-Net, and MCNN, respectively. Additionally, the RMSE was 4.44, 22.13, and 15.79 lower compared to the three aforementioned methods. This research facilitated a relatively accurate counting of apple flower clusters at the tree-level in orchards, offering valuable information for tasks including flowering monitoring, load estimation, and yield forecasting.