Intelligent Psyllid Monitoring Based on DiTs-YOLOv10-SOD

Citrus psyllids are common pests that feed on the sap of citrus trees, leading to yellowing, deformation, and potentially tree death in severe cases. Effective identification and monitoring of these pests are crucial for the health and sustainable development of the citrus industry. Rapid and accurate detection enables farmers to control citrus psyllid infestations promptly, thereby protecting their crops and ensuring industry sustainability. In this article, we utilize a custom-built pest-trapping device to capture the psyllids and upload the image to a server via the Internet of Things. We captured 420 images with a resolution of 3820 × 2160 using the device. These images, containing various types of pests, were utilized for model experimentation and training. On the server, the diffusion transformer (DiT) is utilized to increase the training data, addressing challenges such as limited sample size and class imbalance. A small object detection head is integrated into YOLOv10 to enhance the capture of shallow features in psyllid images. In addition, the soft nonmaximum suppression method is applied to resolve overlapping issues in counting the psyllids. Finally, the results are uploaded to an app, allowing users to stay informed about citrus pest conditions in real time. The experimental results indicate that DiTs-generated images achieved scores of 76.79, 0.29, and 1.68 in the Frechet inception distance, learned perceptual image patch similarity, and multiscale structural similarity metrics, respectively, outperforming the commonly used DDPM model by 8.51, 0.18, and 0.34, respectively. The improved YOLOv10 model, trained with the expanded DiTs dataset, reached a recall, F1-score, and precision of 90.55%, 92.18%, and 93.88%, respectively, demonstrating outstanding performance across all metrics. This approach enables fully automated recognition of citrus psyllids, facilitating real-time detection and contributing to the protection of citrus crops.