A Novel Nectarine Fruit Maturity Detection and Classification Counting Model Based on YOLOv8n

Fruit yield assessment is an important aspect of orchard management. In this context, target detection of fruit is of paramount importance. However, due to complex factors in real orchard environments, such as fruit occlusion, insufficient lighting, and overlapping fruits, traditional detection and counting methods often suffer from low detection accuracy and inadequate classification precision, failing to meet the requirements of practical applications. To address this issue, we focus on nectarine fruit and propose an improved YOLOv8n-based object detection algorithm model, YOLOv8n-global feature extraction enhancement (GFE). We integrate the effective squeeze-and-excitation attention mechanism into the YOLOv8n model. This integration allows our approach to adaptively adjust the weight of each channel, which enhances both detection efficiency and target recognition accuracy. Then, we introduce focal distance-intersection over union loss to address the misjudgment of hard samples. This further contributes to improving detection accuracy. In addition, we incorporate the gather-and-distribute mechanism from GOLD-YOLO, replacing the traditional feature pyramid network structure. This enhancement improves the information fusion capability in the neck of the model, leading to a higher mean average precision (mAP@0.5). In addition, the output of the improved model can be used as an input to DEEPSORT to classify and count nectarine fruit. This functionality can be used for estimating fruit maturity and yield in orchards. Experimental results demonstrate that the YOLOv8n-GFE model achieves a mAP@0.5 of 92.5%, which is an improvement of 3.2% over the original YOLOv8n model, meeting the required accuracy for recognizing nectarine fruit maturity in practical applications.