# ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages
## 4 Experiments and Results
### Contrasting Model Performance Prior to and Following Enhancement  
Table 3 provides a contrast of the comprehensive recognition functions of the benchmark models YOLOv9c and ASHM-YOLOv9. A comparison of precision data is presented: the overall precision, recall, mAP50, and mAP50-95 for strawberry recognition, as well as key parameters such as the quantity of model parameters, model size, and computational load. The experiments show that our ASHM-YOLOv9 achieves an average precision of 97.7% for multistage strawberry recognition, showing a 0.6% enhancement over the benchmark model YOLOv9c. The average Recall is 97.2%, an increase of 2.7%; the mAP50 is 99.1%, an improvement of 0.6%; and the mAP50-95 is 90.7%, an increase of 7.2%. The parameters are 58.29 MB, which is a reduction of 2.21 MB compared with before; the model size is 115.19 MB, a decrease of 7.21 MB; and the FLOPs are 253.3 G, which is a reduction of 10.6 G compared with before. In addition, Tables 4-7 show a comparison of precision data between the benchmark models YOLOv9c and ASHM-YOLOv9 across three stages: flowering, growing, and ripening. In contrast to the original model, our model shows improvements in precision during the flowering stage as follows: an increase of 0.6% in precision, 2.8% in recall, 0.9% in mAP50, and 8.1% in mAP50-95. In the growing stage, the improvements are as follows: 1.2% in precision, 3.6% in recall, 1.0% in mAP50, and 7.2% in mAP50-95. In the ripening stage, the improvements are 0.1% in precision, 2.5% in recall, no change in mAP50, and a 6.9% increase in mAP50-95. From the data comparison, it is evident that our precision metrics, with the exception of mAP50 in the ripening stage, which remains consistent with the original model, outperform the original model in all other aspects, particularly showing the most significant improvement during the growing stage. This comparison indicates that our ASHM-YOLOv9 not only leads the original model in terms of precision across all stages in the multistage recognition task for strawberries but also outperforms it in terms of the quantity of parameters and resource utilization effectiveness. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Flower Stage/\%**} \\ \hline
**Models** & **Precision** & **Recall** & **mAP50** & **mAP50-95** \\ \hline YOLOv5s & 97.5 & **97.6** & 98.1 & 79.9 \\ YOLOv7 & 96.9 & 95.9 & 97.0 & 80.6 \\ YOLOv8n & 97.2 & 94.0 & 98.4 & 85.7 \\ YOLOv9c & 97.3 & 93.2 & 98.0 & 80.0 \\ ASHM-YOLOv9 & **97.9** & 96.0 & **98.9** & **88.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results for each model at the flower stage. Evaluation of the Detection Performance of ASHM-YOLOv9  
To portray the performance of the base models YOLOv9c and ASHM-YOLOv9 in strawberry recognition, we conducted experiments using the same complex scenes, and the results are presented in Figure 10. The red dashed boxes signify areas where the base model YOLOv9c made incorrect identifications, whereas the improved ASHM-YOLOv9 model recognized them correctly. The yellow arrows represent samples missed during recognition, and the red arrows indicate samples that were incorrectly identified. The filled rectangular boxes represent recognized labels and confidence levels, whereas the unfilled dashed rectangular boxes represent the areas of recognized objects. The varying shades of color represent strawberries at three different stages. In summary, based on the recognition findings, our improved ASHM-YOLOv9 model has more advantages in identifying strawberries at different stages. Critical error analysis reveals primary misclassification patterns, including stage-transition confusion where near-mature fruits with 70-80% coloration are misclassified as the growth stage (red arrows in Figure 10), heavy occlusion cases where fruits are more than 90% obscured (yellow arrows), and low-contrast lighting conditions that wash out color features (dashed boxes).