Elevating Grape Detection Precision and Efficiency with a Novel Deep Learning Model

In the domain of modern agricultural automation, precise grape detection in orchards is pivotal for efficient harvesting operations. This study introduces the Grapes Enhanced Feature Detection Network (GEFDNet), leveraging deep learning and convolutional neural networks (CNN) to enhance target detection capabilities specifically for grape detection in orchard environments. GEFDNet integrates an innovative Enhanced Feature Fusion Module (EFFM) into an advanced YOLO architecture, employing a 16x downsampling Backbone for feature extraction. This approach significantly reduces computational complexity while capturing rich spatial hierarchies and accelerating model inference, which is crucial for real-time object detection. Additionally, an optimized dual-path detection structure with an attention mechanism in the Neck enhances the model's focus on targets and robustness against dense grape detection and complex background interference, a common challenge in computer vision applications. Experimental results demonstrate that GEFDNet achieves at least a 3.5% improvement in mean Average Precision (mAP@0.5), reaching 89.4%. It also has a 9.24% reduction in parameters and a 10.35 FPS increase in frame rate compared to YOLOv9. This advancement maintains high precision while improving operational efficiency, offering a promising solution for the development of automated harvesting technologies. The study is publicly available at: https://github.com/YangxuWangamI/GEFDNet.