Abstract: Papaya cultivation faces numerous challenges from various diseases, highlighting the critical need for accurate classification and effective management strategies. Our research introduces an innovative approach using the YOLOv9c model for automated classification of Papaya Diseases, including Anthracnose, Phytophthora Blight, and others. We meticulously trained the model on a diverse dataset, ensuring robust performance across disease types, and developed a user-friendly web application for instant disease diagnosis, facilitating timely interventions. The efficacy of YOLOv9c in revolutionizing precision agriculture for improved crop sustainability is a central focus of our study. Through extensive field trials conducted under real-world conditions, we validated the model's performance, affirming its reliability and practical utility. This validation underscores the potential for integrating YOLOv9c into existing agricultural systems, offering advanced disease management strategies that can significantly enhance yield outcomes and optimize resource utilization. By leveraging cutting-edge technology like YOLOv9c, we empower farmers with accurate and timely disease diagnosis tools, ultimately promoting food security and economic stability in papaya cultivation. This work aligns with broader efforts to harness technology for sustainable agriculture, benefiting both farmers and the environment.
Keywords: Papaya Disease Classification, Machine learning, Disease classification, Deep learning, Papaya, Computer vision.
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DOI:
10.17148/IJARCCE.2024.13384
