Image-Based Corn Leaf Disease Detection Framework using Yolov8 Model - Nerissa L. Javier (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Crop diseases pose significant challenges to agricultural production, leading to substantial crop losses. With the increasing demand for food production to meet the needs of a growing population, ensuring the health and productivity of crops like corn is of paramount importance. The proposed framework integrates cutting-edge technologies including computer vision, machine learning, and mobile application development to create a user-friendly and efficient tool to accurately identify and classify diseases affecting corn crops. The framework aims to automate the disease detection process through the analysis of images of corn leaves affected by diseases like northern leaf blight (NLB), gray leaf spot (GLS), and northern leaf spot (NLS) obtained from Kaggle datasets. By utilizing Yolov8 for feature extraction and classification, the system achieves high accuracy in disease detection. The framework is designed to be scalable, adaptable, and efficient, making it suitable for real-time applications in agriculture. Experimental results demonstrate the effectiveness of the proposed approach in accurately diagnosing corn diseases, thereby aiding farmers in timely intervention and crop management. By integrating these advanced technologies into a comprehensive framework, the corn disease detection mobile application aims to provide farmers with a reliable tool for early diagnosis, effective intervention, and improved crop management practices, ultimately enhancing crop yield and ensuring food security.