A Detection Algorithm for Citrus Huanglongbing Disease Based on an Improved YOLOv8n

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 14; p. 4448
• Main Authors: Xie, Wu, Feng, Feihong, Zhang, Huimin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.07.2024
• Subjects: Artificial intelligence; Citrus fruits; Citrus Huanglongbing; deep learning; Neural networks; object detection; orchard management; Telematics; YOLOv8n; Citrus Huanglongbing; deep learning; object detection; YOLOv8n; orchard management
• DOI: 10.3390/s24144448
• ISSN: 1424-8220

Given the severe impact of Citrus Huanglongbing on orchard production, accurate detection of the disease is crucial in orchard management. In the natural environments, due to factors such as varying light intensities, mutual occlusion of citrus leaves, the extremely small size of Huanglongbing leaves, and the high similarity between Huanglongbing and other citrus diseases, there remains an issue of low detection accuracy when using existing mainstream object detection models for the detection of citrus Huanglongbing. To address this issue, we propose YOLO-EAF (You Only Look Once–Efficient Asymptotic Fusion), an improved model based on YOLOv8n. Firstly, the Efficient Multi-Scale Attention Module with cross-spatial learning (EMA) is integrated into the backbone feature extraction network to enhance the feature extraction and integration capabilities of the model. Secondly, the adaptive spatial feature fusion (ASFF) module is used to enhance the feature fusion ability of different levels of the model so as to improve the generalization ability of the model. Finally, the focal and efficient intersection over union (Focal–EIOU) is utilized as the loss function, which accelerates the convergence process of the model and improves the regression precision and robustness of the model. In order to verify the performance of the YOLO-EAF method, we tested it on the self-built citrus Huanglongbing image dataset. The experimental results showed that YOLO-EAF achieved an 8.4% higher precision than YOLOv8n on the self-built dataset, reaching 82.7%. The F1-score increased by 3.33% to 77.83%, and the mAP (0.5) increased by 3.3% to 84.7%. Through experimental comparisons, the YOLO-EAF model proposed in this paper offers a new technical route for the monitoring and management of Huanglongbing in smart orange orchards.