YO-AFD: an improved YOLOv8-based deep learning approach for rapid and accurate apple flower detection.

Abstract

The timely and accurate detection of apple flowers is crucial for assessing the growth status of fruit trees, predicting peak blooming dates, and early estimating apple yields. However, challenges such as variable lighting conditions, complex growth environments, occlusion of apple flowers, clustered flowers and significant morphological variations, impede precise detection. To overcome these challenges, an improved YO-AFD method based on YOLOv8 for apple flower detection was proposed. First, to enable adaptive focus on features across different scales, a new attention module, ISAT, which integrated the Inverted Residual Mobile Block (IRMB) with the Spatial and Channel Synergistic Attention (SCSA) module was designed. This module was then incorporated into the C2f module within the network's neck, forming the C2f-IS module, to enhance the model's ability to extract critical features and fuse features across scales. Additionally, to balance attention between simple and challenging targets, a regression loss function based on Focaler Intersection over Union (FIoU) was used for loss function calculation. Experimental results showed that the YO-AFD model accurately detected both simple and challenging apple flowers, including small, occluded, and morphologically diverse flowers. The YO-AFD model achieved an F1 score of 88.6%, mAP50 of 94.1%, and mAP50-95 of 55.3%, with a model size of 6.5 MB and an average detection speed of 5.3 ms per image. The proposed YO-AFD method outperforms five comparative models, demonstrating its effectiveness and accuracy in real-time apple flower detection. With its lightweight design and high accuracy, this method offers a promising solution for developing portable apple flower detection systems.