<![CDATA[This research is based on the main problem in agriculture, which is plant diseases that can cause significant losses. Rapid and accurate plant disease detection is crucial in controlling and preventing the spread of diseases. In recent years, advancements in computer technology and image processing have led to the application of artificial intelligence methods in plant disease detection and identification. RetinaNet method is proposed as an effective solution for automating plant disease detection. This method utilizes neural networks to detect and classify objects in images. The research will employ image segmentation techniques, such as blob analysis and color modeling, to prepare the training and testing data. By applying RetinaNet method and proven image segmentation techniques, this study aims to develop an accurate and widely applicable automated plant disease detection system in the agricultural industry. The findings of this research are expected to contribute to the development of AI-based agricultural technology and serve as a foundation for further research in this field]]>
