<![CDATA[This study addresses the complex challenge of detecting Huanglongbing (HLB) disease in citrus leaves, which is known as one of the most lethal plant diseases with no known cure. The primary issue in HLB detection is the difficulty in identifying symptoms early and accurately, particularly in dynamic and uncontrolled field environments. Therefore, the main focus of this research is the development of a real-time detection approach using the YOLO V8 algorithm to more accurately detect and classify HLB symptoms in citrus leaf images. The objective of this study is to design a technique that can enhance the detection of HLB disease and compare its performance with the conventional YOLO V8 method. This research also aims to address the limitations of previous studies that used the Support Vector Machine (SVM) method, which only achieved an accuracy of 80%. To achieve this objective, the study utilizes a dataset consisting of 1200 citrus leaf images, representing various levels of severity, including mild, moderate, severe, and healthy leaves. The method employed in this research involves the use of the YOLO V8 algorithm to detect and classify HLB symptoms in citrus leaf images. This approach was tested through a series of experiments to measure accuracy, precision, recall, and computational efficiency. The experimental results consistently demonstrate that the developed approach outperforms the basic YOLO V8 and previous methods using SVM, with an improvement in HLB disease detection accuracy reaching 98%. This study provides critical insights into early detection of HLB disease, potentially serving as a powerful tool to support efforts in preventing the spread of this disease across citrus orchards. Additionally, this research opens opportunities for further development in real-time plant disease detection by integrating more advanced AI technologies and applying similar methods to other plant diseases. Future research can focus on developing more efficient and scalable algorithms for use in various field conditions, as well as exploring the integration of sensors and IoT technology for more comprehensive plant health monitoring.]]>
