Peach Flower Density Detection Based on an Improved CNN Incorporating Attention Mechanism and Multi-Scale Feature Fusion

Abstract

Flower thinning for fruit trees in time is an important operation to keep a suitable quantity of fruits and guarantee the quality of fruits. Accurate detection of flower density is the premise of precise flower thinning, and machine vision provides an effective approach to achieving the accurate identification of flower density. To detect the flower density on the proximal side of Y-shaped densely planted peach trees accurately, this study proposed a method based on an RGBD camera and a convolutional neural network that incorporated an attention mechanism and multi-scale feature fusion. Firstly, image acquisition and preprocessing were performed with the RGBD camera, and the complex background and distal flowers were filtered out through depth information. Then, a convolutional neural network for flower density detection based on an attention mechanism and multi-scale feature fusion, named the flower counting network (FC-Net), was constructed and tested. Results showed that the coefficient of determination (R2) between the estimated number of flowers by the FC-Net and the real values reached 0.95, the mean absolute error (MAE) was 4.3, the root mean square error (RMSE) was 5.65, the counting error rate (Er) was 0.02%, and the processing time of one image was 0.12 s. The proposed FC-Net can provide visual support for intelligent mechanical flower thinning operations.

Keywords

• deep learning
• flower detection
• image processing
• flower thinning
• depth information
• RGBD camera