关键词: 澳洲坚果, 果园监测, 深度学习, 改进YOLOv4, 目标检测

Abstract: In response to the management difficulty of largescale Macadamia nuts orchards,  a forest Macadamia nut growth monitoring method based on improved YOLOv4 was proposed. Image acquisition was carried out in a Macadamia plantation, where three common forms of Macadamia presence were recorded to produce a VOC dataset and used for model training. Data augmentation was applied to classes with fewer samples to equalize the distribution of training samples. Improved on the basis of  the original YOLOv4 method, DenseNet121 network was used to replace the original backbone network, and Focal loss was used to optimize the classification loss function of the detection model, which effectively improved the detection model accuracy and alleviated the problem of unbalanced detection accuracy between classes. The experimental results showed that the improved YOLOv4 method had the highest average precision (AP) for each Macadamia nut form, compared to the YOLOv4 and YOLOv3, the mean average precision (mAP) of the detection model reached 93.33% and the detection speed reached 28.7 FPS, which achieved the realtime and efficient acquisition of growth information of Macadamia nut, such as Macadamia nut drop and disease in orchards, and provided a basis for Macadamia nut growth monitoring.

Key words:  , Macadamia nuts, orchard monitoring, deep learning, improved YOLOv4, object detection