## 2 Materials and Methods
### Experiment  
To validate the effectiveness of the proposed model and image-processing algorithm in this study, four sets of experiments were conducted sequentially.  
In the first set of experiments, the improved YOLOv8 model proposed in this study, along with several common deep learning network models, such as models of YOLOv3, YOLOv4, YOLOv5n, YOLOv7, YOLOX, YOLOv8n, SSD-vgg16, and Faster R-CNN, was  
Figure 10: Flow chart of drawing strawberry growth information map.  
Figure 9: Changes in strawberry growth stage.  
trained using the same dataset. A comparative performance analysis of the models was conducted. The hardware environment of the experiment was mainly a computer equipped with an Intel i5-13600 kf processor, 32 GB RAM, and GeForce GTX 4080 GPU. The computer used CUDA 11.2 parallel computing architecture and NVIDIA cuDNN 8.0.5 GPU acceleration library. The software simulation environment was the Pytorch deep learning framework (Python version 3.10). Anaconda was used to configure and manage the virtual environment, and Pycharm was used to compile and run programs. Model performance metrics mainly included P (precision), R (recall), F1 (harmonic average), AP (average precision), [EMAIL_ADDRESS] (mean average precision), as shown in Equations (15)-(19).  
\[\text{precision}=\frac{\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{F}_{ \text{P}}} \tag{15}\]  
\[\text{recall}=\frac{\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{F}_{\text {N}}} \tag{16}\]  
\[\text{F}_{1}=\frac{2\times\text{precision}\times\text{recall}}{\text{precision}+ \text{recall}} \tag{17}\]  
\[\text{AP}=\frac{\sum\text{precision}}{\text{N}} \tag{18}\]  
\[\[EMAIL_ADDRESS]}=\frac{\sum_{i=1}^{\text{K}}\text{AP}_{i}}{\text{N}_{\text{C}}} \tag{19}\]  
where T\({}_{\text{P}}\) represents the number of strawberries correctly identified, F\({}_{\text{P}}\) represents the number of strawberries incorrectly identified, F\({}_{\text{N}}\) represents the number of missed strawberries, N represents the total number of images, and N\({}_{\text{C}}\) represents the number of categories of the strawberry maturities. AP represents that the integral of accuracy rate to recall rate is equal to the area under the P-R curve, and [EMAIL_ADDRESS] is the average of the average precision of all categories.  
The second set of experiments was used to segment plant areas using the proposed environment stability-based region segmentation method, based on different color spaces of RGB, HSV, and Lab, in order to find the best color space type suitable for threshold segmentation. The overlap between the proposed method and the actual manually calibrated area was compared and analyzed to determine the optimal color space suitable for this algorithm, and the segmentation effect of the proposed algorithm was tested.  
In the third set of experiments, edge extraction and peak detection algorithms were used to process the segmented strawberry plant area, estimate the number of plants in the area, and compare it with the actual number of plants. A total of fifty drone images taken from a height of 2 m were located in the first group, and fifty drone images taken from a height of 3 m were located in the second group. Errors were recorded and analyzed.  
In the fourth set of experiments, the regional growth map was drawn. According to the actual geographic information and shooting sequence captured by the drone, all regional growth maps were concatenated, and the overall strawberry growth map of the farmland was drawn. The picking priority information of the growth map was observed and analyzed. A total of ten strawberry cultivation experts assigned picking priorities to different areas of the entire strawberry field based on their experience (a total of 5 levels). A comparative analysis was conducted between these priorities and the growth charts generated in this study.