Kiwi-YOLO: A Kiwifruit Object Detection Algorithm for Complex Orchard Environments

To address the challenges of poor model adaptability and high computational complexity in complex orchard environments characterized by variable lighting, severe occlusion, and dense fruit clusters, an enhanced kiwifruit detection network, Kiwi-YOLO, is proposed based on YOLOv8. Firstly, replacing the main network with the MobileViTv1 module reduces computational load and parameters, thus enhancing inference efficiency for mobile deployment. Secondly, incorporating BiFPN into the model’s neck as a replacement for PANet improves feature distinguishability between background regions and target instances. Additionally, incorporating MCA module promotes cross-dimensional feature interactions, strengthening model robustness and generalization performance. Finally, the MPDIoU loss function is adopted to minimize bounding box vertex distances, mitigating detection box distortion caused by sample heterogeneity while accelerating convergence and enhancing localization accuracy. Experimental results indicate that the enhanced model achieves improvements of 2.1%, 1.5% and 0.3% in precision, recall, and mAP, respectively, over the baseline YOLOv8, while reducing parameters (Params) and computational complexity (GFLOPs) by 19.71 million and 2.8 billion operations. Moreover, it surpasses other comparative models in performance. Furthermore, in experiments detecting kiwifruit targets under complex lighting and occlusion conditions, the Kiwi-YOLO model demonstrated excellent adaptability and robustness. Its strong environmental adaptability provides technical guidance for advancing the practical application of unmanned intelligent kiwifruit harvesting.