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In order to realize the adaptive grading conveyance and real-time monitoring of harvesting status in the process of Panax notoginseng combined harvesting operation, focusing on the characteristics of Panax notoginseng root-soil complex and the complex field harvesting conditions, a Panax notoginseng object detection method based on YOLO v8s and suitable for deployment on the Jetson Nano was proposed. Based on the accurate recognition of Panax notoginseng by YOLO v8s, the channel pruning algorithm was utilized to formulate a corresponding pruning strategies for its new model structural characteristics, which ensured the accuracy and improved the real-time detection performance at the same time. The improved model was deployed to Jetson Nano by using the TensorRT inference acceleration framework, which realized the flexible deployment of the Panax notoginseng object detection model. The experimental results showed that the mean average precision of the improved PN-YOLO v8s-Pruned model on the host side was 93.71%, although it was decreased by 0.94 percentage points compared with that of the original model, the number of parameters, computational complexity, and model size were 39.75%, 57.69%, and 40.25% of the original model, respectively, and the detection speed was increased by 44.26%. Compared with other models, the improved model demonstrated superior comprehensive detection performance in terms of computational complexity, detection accuracy, and real-time performance. After deployment at the Jetson Nano, the improved model had a detection speed of 18.9 frames per second, which was 2.7 times higher than before acceleration and 5.8 frames per second higher than the original model, and the deployment detection effect was better than the original model. The results of the bench tests showed that the mean average precision of Panax notoginseng detection was more than 87% under four conveyor separation harvesting conditions. The average accuracy of the Panax notoginseng counting under different conveyor separation harvesting conditions and different flow levels reached 92.61% and 91.76%, respectively. The field test results showed that the mean average precision of Panax notoginseng detection was more than 84%, and the average accuracy of the Panax notoginseng counting reached 88.11%, which could meet the detection requirements of Panax notoginseng under complex field harvesting conditions, and could provide technical support for the monitoring system of harvesting quality and the adaptive grading transportation system of combined harvesting operation based on edge computing equipments.