# ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages
## 6 Conclusions  
In this research, we present ASHM-YOLOv9, a multistage object detection model for strawberry plants. On the basis of YOLOv9, we introduce AKConv, which improves the computing speed and reduces the size and number of parameters. The addition of the SE network enhances the model's feature selection and fusion capabilities. Moreover, the original model's ADown downsampling algorithm is replaced with the improved HWD-ADown algorithm, which enhances identification precision and recall. The MPDIoU loss function addresses the issue where the predicted and annotated boxes have matching aspect ratios but differing dimensions, enabling better object border identification and further improving detection precision. Based on the above characteristics, the ASHM-YOLOv9 model, which has less computational complexity, fewer parameters and models, and lower detection precision than the original YOLOv9 model, was developed. By analyzing the experimental results, the following conclusions are drawn:  
(1) In the self-built greenhouse strawberry dataset, under the same experimental conditions, the improved ASHM-YOLOv9 model increased the precision by 0.6% and the recall by 3.0% compared with those of the original model YOLOv9c. When the mAP50 increased by 0.7%, the mAP50-95 increased by 7.4%, the number of parameters decreased by 3.7%, the model size decreased by 5.6%, and the floating-point calculations decreased by 3.8%, effectively improving the performance of the original model. Compared with the YOLOv5s, YOLOv7, and YOLOv8n models, the precision increases by 0.4%, 1.0% and 0.5%, respectively; the recall increases by -0.1%, 2.2%, 2.1%, and 3.0%, respectively; the mAP50 increases by 0.3% and 2.6%, 0.7%, 0.7%, respectively; and the mAP50-95 increases by 7.9%, 8.1%, 5.0%, and 7.4%, respectively. (2) To verify the detection performance of the ASHM-YOLOv9 model, three sets of comparison experiments were carried out for visualization analysis. The results demonstrate that our model attains the highest identification precision in both the normal environment and the complex environment; fruit boundary identification also has a good effect. This study focused on conventional strawberry varieties commonly available on commercial markets. To develop a more robust and generalizable detection system, future research will systematically expand the dataset to include diverse strawberry cultivars (white varieties, day-neutral types, alpine strawberries) and extend the ASHM-YOLOv9 framework to other greenhouse crops such as tomatoes, peppers, and small fruit crops through transfer learning approaches. Additionally, multi-environment validation across different greenhouse facilities with varying lighting and growing conditions will be conducted to ensure model robustness and practical applicability, ultimately establishing a comprehensive crop detection platform for diverse agricultural production systems. Beyond the technical achievements, this research contributes to addressing broader societal challenges in agricultural sustainability and food security. The automated phenotyping system promotes precision agriculture practices that optimize resource utilization while reducing environmental impact through targeted interventions based on accurate growth stage identification. However, the implementation of such technologies must consider ethical implications, including data privacy, equitable access for small-scale farmers, and potential employment transitions in agricultural sectors. Future deployment should prioritize inclusive development approaches that ensure technology benefits reach diverse farming communities while preserving traditional agricultural knowledge and supporting workforce adaptation to emerging precision agriculture paradigms. Conceptualization, writing-original draft preparation, Y.M. ; validation, S.B. ; software, W.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China, grant number 62261038.