## 4 Discussion  
The drone image has the characteristics of high resolution, wide coverage, and multiple and small targets to be identified. The improved YOLOv8 model added two additional small object detection layers and set the img_size of the input image to 1280. During the training process, the size of the target feature map was enlarged, which was beneficial for the model to learn target features. Adding a detection layer of small targets could improve the detection effect on small targets, and similar conclusions could be found in the article [26]. The precision and recall of the proposed model have significantly improved, which reflects the proposed model had high detection accuracy. The performance curve of the proposed model showed little fluctuation during the training process, which indicated that the model could effectively learn strawberry features at different stages and the training was efficient. This accounted for the advantages of the shuffle attention module application, which effectively learned the relationship between different channel information at the same position of the strawberry target, as well as the spatial information between different positions. The module enabled the target features to be effectively obtained by the proposed model, while also distinguishing the differences in features between different positions, improving the detection accuracy of the model and enhancing its stability and robustness. The shuffle attention module could improve the learning performance of the model, and similar conclusions can be found in the article [27]. The VOV-GSCSP module could reduce network complexity and maintain certain accuracy in small object detection, and a similar conclusion can be found in the article [28]. The additional small object detection layer brought about an increase in model volume, which could be optimized through the VOV-GSCSP module, giving the proposed model advantages such as small model volume, high recognition accuracy, and fast detection speed. It could be effectively applied to the recognition of strawberry targets at different growth stages in drone images. The target of segmentation was the black plastic film area, which had stable color in different environments and was easy to segment. Therefore, the method of obtaining the plant area through the plastic film area was accurate and efficient. The RGB color space mixed lighting components into three color channels. For area segmentation in drone images with lighting changes, the boundaries of the segmented areas were unclear and could not be further applied to plant area segmentation. While the Lab color space considered lighting as a separate component, it however had the characteristic of uniform perception. The color changes at equal intervals are the same, making it difficult to effectively distinguish between noise and target areas, resulting in more noise in the segmentation results. Based on the segmentation of HSV color space, while considering lighting separately, the noise was eliminated by integrating changes in hue and saturation, accurately segmenting black film areas in different environments. HSV color space-based segmentation could be adapted to lighting and reduce noise. A similar conclusion can be found in the article [29]. Based on the proposed method of combining edge extraction and peak detection, the number of strawberry plants in drone images was estimated. The calculated values were generally lower than the actual values, and the reason may be that during the peak detection process, the curve amplitude of some continuous plants changed similarly, and multiple peaks were detected as one peak, introducing errors. However, this method could generally accurately count the number of plants in a region and, the smaller the area, the better the detection effect. By observing the overall growth information map of the strawberry field, harvesting plans could be formulated based on the picking priority in different regions.