## 5 Conclusions  
This study proposed a lightweight deep learning model based on YOLOv8n improvement, which accurately identified strawberries at different growth stages in drone images. A region segmentation algorithm based on environmental stability was proposed to effectively segment the strawberry plant region. A plant counting method based on edge extraction and peak and valley detection was proposed to estimate regional plant numbers. Based on the above methods, a growth information map was created to visualize the growth situation of different areas of strawberry fields and assist farmers in formulating harvesting plans. The main conclusions are as follows.  
An improved YOLOv8 model was constructed by replacing the neck network of the YOLOv8n model with the VOV-GSCSP block, applying the shuffle attention block, and adding two small object detection layers.
2. The Improved YOLOv8 model demonstrated an average accuracy of 82.5% in identifying immature strawberries, 87.4% for mature ones, and 82.9% for strawberry flowers in drone images. The model's performance metrics included a mean average precision (mAP) of 84.2%, precision (P) of 93.4%, recall of 94%, and an F1 score of 93.69%. Additionally, the model exhibited an average detection speed of 6.2 ms and had a compact model size of 20.1 MB.
3. The region segmentation algorithm based on environmental stability showed performance in which the average proportion of the segmentation area to the actual area was 86.12% for the plastic film area surrounding the plant.
4. The proposed method for estimating the number of plants by combining edge extraction and peak detection had a counting error within 1 in drone images at a height of 2 m, and within 3 in drone images at a height of 3 m.
5. The created growth information map of the strawberry field could provide intuitive information on the recent maturity of strawberries in different regions and assist farmers in determining the harvesting priorities in different areas during peak picking periods; it would also help them when developing harvesting plans, and would drive improvements in the quality of harvested strawberries.  
These conclusions confirmed that the proposed model with the image-processing technology had effective performance for detecting strawberry objects in drone images and creating the growth information map of strawberries. Future research will capture continuous images of strawberry plants throughout their growth stages, utilize the method proposed in this study to quantify harvest indices, and combine them with the nutritional status of the plants for analysis, providing valuable insights for farmers.  
Conceptualization, C.W., Q.H. and C.L.; methodology, C.W., F.W. and J.L.; investigation, Q.H., D.K. and C.L.; resources, C.W., F.W. and X.Z.; writing--original draft preparation, Q.H.; writing--review and editing, C.W. and Q.H.; project administration, J.L. and D.K.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grants from the National Natural Science Foundation of China (52005069), the Guangdong Basic and Applied Basic Research Foundation (2022A1515140162), the Yunnan Major Science and Technology Special Plan (202302 AE090024), and the Yunnan Fundamental Research Projects (202101 AT070113).  
Not applicable.  
The data presented in this study are openly available at [[https://github.com/abysswatcher-hqy/I-YOLOv8-model](https://github.com/abysswatcher-hqy/I-YOLOv8-model)]([https://github.com/abysswatcher-hqy/I-YOLOv8-model](https://github.com/abysswatcher-hqy/I-YOLOv8-model)) (accessed on 1 January 2020).  
We declare that we do not have any commercial or associative interests that could be viewed as representing a conflict of interest in the work submitted.