STRAWBERRY FRUIT DETECTION METHOD BASED ON IMPROVED YOLOv8N

As an economic crop of Rosaceae family, strawberry has the advantages of short reproductive cycle, wide ecological adaptability and significant economic benefits, and its planting industry has been rapidly developed in recent years. Aiming at the low efficiency and high labor cost of traditional manual picking detection methods in the intelligent transformation of strawberry industry, this study innovatively proposes a strawberry fruit intelligent detection system based on YOLOV8N. By introducing RFAConv dynamic sensory field convolution, SENet channel attention mechanism and InceptionNeXt lightweight structure, combined with Wise-IoU loss function and DIoU-NMS post-processing algorithm, the synergistic enhancement of detection accuracy and computational efficiency is realized. The ablation experiments show that the improved model has a precision rate of 95.92%, a recall rate of 95.45%, and a mAP50 of 98.29% on the strawberry dataset, which are 4.14%, 3.31%, and 1.55% higher than that of the baseline model, respectively, while the number of model parameters is compressed to 5.17 M (a reduction of 12.96%). This research can provide technical support for intelligent strawberry picking.