Red Raspberry Maturity Detection Based on Multi-Module Optimized YOLOv11n and Its Application in Field and Greenhouse Environments

In order to achieve accurate and rapid identification of red raspberry fruits in the complex environments of fields and greenhouses, this study proposes a new red raspberry maturity detection model based on YOLOv11n. First, the proposed hybrid attention mechanism HCSA (halo attention with channel and spatial attention modules) is embedded in the neck of the YOLOv11n network. This mechanism integrates halo, channel, and spatial attention to enhance feature extraction and representation in fruit detection and improve attention to spatial and channel information. Secondly, dilation-wise residual (DWR) is fused with the C3k2 module of the network and applied to the entire network structure to enhance feature extraction, multi-scale perception, and computational efficiency in red raspberry detection. Concurrently, the DWR module optimizes the learning process through residual connections, thereby enhancing the accuracy and real-time performance of the model. Finally, a lightweight and efficient dynamic upsampling module (DySample) is introduced between the backbone and neck of the network. This module enhances the network’s multi-scale feature extraction capabilities, reduces the interference of background noise, improves the recognition of structural details, and optimizes the spatial resolution of the image through the dynamic sampling mechanism. Reducing network parameters helps the model better capture the maturity characteristics of red raspberry fruits. Experiments were conducted on a custom-built 3167-image dataset of red raspberries, and the results demonstrated that the enhanced YOLOv11n model attained a precision of 0.922, mAP@0.5 of 0.925, and mAP@0.5 of 0.943, respectively, representing improvements of 0.7%, 4.4%, and 4.4%, respectively. At 3.4%, mAP@0.5-0.95 was 0.798, which was 2.0%, 9.8% and 3.7% higher than the original YOLOv11n model, respectively. The mAP@0.5 of unripe and ripe berries was 0.925 and 0.943, which was improved by 0.7% and 4.4%, respectively. The F1-score was enhanced to 0.89, while the computational complexity of the model was only 8.2 GFLOPs, thereby achieving a favorable balance between accuracy and efficiency. This research provides new technical support for precision agriculture and intelligent robotic harvesting.