## 4 Conclusions  
This study proposes an improved model of HM-YOLO aimed at enhancing the detection performance of strawberry ripeness in complex environments. Firstly, the PCIB was designed to replace the bottleneck structure in the C2f module of the backbone network, thereby effectively extracting the key features of strawberries at different ripeness stages. Simultaneously, the PHAM was developed to handle the long-range dependencies and accurately locate strawberry fruit target regions at a lower computational cost. In the neck network, we integrated the MS-FPN with PHAM to precisely discern and efficiently extract the fine-grained features of strawberries of various sizes. Finally, the Focaler-shape-IoU method was proposed to address the issue of the imbalanced difficulty among samples at different ripeness stages, thereby further improving the accuracy of the boundary box regression. Data augmentation was employed to expand 5600 images of strawberries grown in complex natural environments to 16800 images. The test results indicate that the HM-YOLO model achieved a precision of 92.1% and mAP0.5 value of 92.7%, outperforming the original YOLOv8n model. Therefore, this technology holds significant potential for improving the quality of machine-harvested strawberry fruits.  
However, several challenges must be considered for practical field deployment. Firstly, the model's reliance on high-quality image data may encounter limitations in real-world agricultural settings, in which factors such as lighting conditions, occlusions, and varying growth patterns can affect system accuracy. These challenges call for the further refinement of data augmentation strategies and improvements in model robustness to ensure a reliable performance under diverse environmental conditions. Additionally, the need for real-time processing in outdoor field environments imposes additional constraints on computational resources. This necessitates the further optimization of the model to run efficiently on edge devices with a limited processing power. Moreover, although the model has been optimized for strawberry ripeness detection, expanding its capability to handle other  
Figure 12: Recognition outcomes of YOLOv8n-GhostNetv2 (**a**), YOLOv10n (**b**), Libra R-CNN (**c**), and our model (**d**), respectively.  
agricultural tasks--such as detecting fruit in dense or heavily occluded plantings--remains an important area for future development. Future research will also explore integrating the model with robotic systems for autonomous strawberry picking, taking into account the dynamics of machine mobility and interactions with the environment.  
Despite these advancements, this study also presents certain limitations. Firstly, while the model categorizes strawberry ripeness into three stages, the complexity of the ripening process means that this simplification may not fully capture the continuous and gradual changes that occur throughout a strawberry's growth cycle. To address this, future work will focus on refining the ripeness classification system, potentially incorporating more detailed stages or adopting continuous prediction models to better reflect the dynamic evolution of strawberry ripening. Secondly, although data augmentation techniques have been employed to improve the model's robustness, the optimal image enhancement thresholds that account for the diverse morphological variations in natural strawberry growth have not yet been fully optimized. Future research will aim to fine-tune these thresholds, enhancing the model's performance across a broader range of natural growth conditions and ensuring better generalization to varying environmental factors.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.