# A Strawberry Ripeness Detection Method Based on Improved YOLOv8
## 2 Materials and Methods
### Network Model Architecture for Strauberry Ripeness Detection
#### 2.3.2 DySample  
In orchard environments, strawberries at various ripeness levels are often intermingled, with a common occurrence of adhesion between ripe fruits, making precise segmentation particularly challenging. Although the YOLOv8 object detection algorithm has demonstrated notable improvements in performance, it still employs traditional bilinear interpolation for upsampling. This approach inevitably results in the loss of fine-grained details, especially in scenarios involving overlapping or adhered strawberries, where the issue becomes more pronounced. Figure 6: YOLOv8 network structure diagram with CA. To address this limitation, this study introduces DySample as an alternative upsampling method to enhance YOLOv8's capability in capturing fine details of strawberry fruits. DySample offers high efficiency, low computational overhead, and improved accuracy, making it well suited for retaining critical features such as fruit contours, size, and shape. Its integration into the YOLOv8 architecture significantly improves detection accuracy and robustness in complex orchard environments. The implementation and optimization effects of DySample within the YOLOv8 framework are illustrated in Figure 7.  
The Table 1 presents a comparative evaluation of various upsampling methods integrated with the Faster R-CNN framework on the MS COCO dataset. Notably, the DySample approach demonstrates superior detection performance while maintaining parameter efficiency. For instance, with the ResNet-50 backbone, DySample+ achieves an AP (average precision) of 38.7%, outperforming conventional methods such as Nearest and Deconv, particularly in stringent metrics like AP\({}_{75}\) and across small to large object scales. Moreover, using a more powerful ResNet-101 backbone, DySample+ further improves AP to 40.5%. These results substantiate the effectiveness of dynamic sampling strategies in enhancing feature upsampling, thereby boosting object detection accuracy and robustness. This outstanding performance is the primary reason why we choose DySample as the key focus for improvement. This aligns with findings reported by [PERSON] et al. (2023) [PERSON] et al. (2023), emphasizing the potential of advanced upsampling techniques in object detection tasks. Figure 7: Detailed design of DySample. DySample dynamically adjusts the sampling strategy by reorganizing features within a predefined region based on variations in feature content. Each spatial location is assigned multiple sampling weights to accommodate different scene requirements. The rearranged features are subsequently combined into spatial blocks to perform feature upsampling. In contrast to conventional bilinear interpolation, which relies exclusively on pixel positions to define the upsampling kernel and disregards the semantic information contained within the feature map, DySample employs an adaptive approach that incorporates semantic context. The bilinear method is relatively \"mechanical,\" relying on fixed spatial positions and failing to capture complex feature distributions. DySample, on the other hand, benefits from a larger receptive field and greater adaptability to diverse feature distributions, without significantly increasing model parameters or computational cost. Accordingly, this study replaces the bilinear upsampling method in YOLOv8 with DySample to enhance the model's feature representation and detection performance. Its implementation within the YOLOv8 framework is illustrated in Figure 8.