## 3 Results and Discussion
### Comparison with State-of-The-Art Models  
To comprehensively evaluate the performance of the HM-YOLO model in strawberry ripeness detection, comparative experiments were conducted between our model and the current state-of-the-art object detection algorithms on the SBDS strawberry dataset. Additionally, a set of special experiments was conducted, using YOLOv8n as the baseline model and comparing it with state-of-the-art backbone networks such as MobileNetV4, EfficientViT, and GhostNetv2. As shown in Table 6, although the Libra R-CNN exceled in performance with mAP\({}_{0.5}\) and mAP\({}_{0.50.95}\) values reaching 87.6% and 71.8%, respectively, its parameter count was as high as 34.3 M and its computational complexity reached 293.6 G, resulting in a lower computational efficiency for this two-stage model. YOLOv10n, an end-to-end network, has a parameter count of only 2.3 M and a computational complexity of 6.7G, but its performance on mAP\({}_{0.5}\) and mAP\({}_{0.50.95}\) values is 0.1% and 0.3% lower than YOLOv8n, respectively. This may be because YOLOv10n uses inexpensive deep convolution and pointwise convolution techniques to replace traditional convolutional downsampling and reduce the overhead of classification heads, which improves its efficiency but decreases its performance. Although our model showed only marginal  
Figure 11: The strawberry object detection results of our model (**b**,**d**) compared with the baseline model (**a**,**c**) on the SBDS dataset. The cyan dashed circles indicate missed or falsely detected objects, while the red, blue, and yellow rectangles represent detected raw, turning, and ripe strawberries, respectively. The confidence scores are labeled at the top of the rectangles. performance improvements compared to other single-stage YOLO algorithms, such as a 2.6% higher mAP\({}_{0.5}\) compared to the classic YOLOv5n, and a 2.0% higher mAP\({}_{0.5}\) compared to the advanced YOLOv9-tiny, it exhibited a clear advantage when compared with models using lightweight backbone networks, particularly MobileNetV4, EfficientViT, and GhostNetv2. Notably, our model outperformed the YOLOv8-GhostNetv2 by 4.6% in the critical mAP\({}_{0.5}\) value. The HM-YOLO model demonstrates outstanding performance in terms of parameter count and computational complexity, which are significantly lower than those of two-stage models such as Libra R-CNN, while maintaining a low computational overhead in single-stage models. This efficiency allows HM-YOLO to run smoothly in resource-constrained environments. Despite the optimization of computational resources, HM-YOLO does not significantly compromise detection performance. Compared to other single-stage models and lightweight backbone networks, HM-YOLO shows significant advantages in the critical mAP\({}_{0.5}\) metric, proving that it can perform computations efficiently while still delivering high-precision detection results. The design philosophy of HM-YOLO makes it highly suitable for real-time applications. In agricultural environments, the real-time detection of strawberry ripeness is crucial for harvest decisions, and the low memory and power requirements enable HM-YOLO to operate efficiently on edge devices, perfectly meeting the demand for quick and accurate detection in applications in agricultural environments. To more intuitively demonstrate the performance of our model in detecting strawberries at different stages of ripeness, three representative networks were selected for comparison on the SBDS dataset: the two-stage network Libra R-CNN, the advanced end-to-end YOLOv10n from the YOLO series, and the single-stage network equipped with the outstanding backbone network GhostNetv2. In Figure 12a, the YOLOv8n-GhostNetv2 failed to identify small strawberries marked with \"r\" and \"s\", so the GhostNetv2-based detectors were not suitable for detecting strawberries in the early, unripe stage.