Hybrid CNN & Random Forest Model for Effective Turmeric Leaf Disease Diagnosis

This study dives into the classification of turmeric leaf diseases to enhance the field of agriculture and crop health management. Turmeric, a major agricultural commodity, is sensitive to a variety of illnesses, causing problems for both yield and quality. In turmeric production, accurate and prompt disease identification is critical for efficient disease management. The data analysis shows a meticulously developed categorization model that has produced impressive results. Precision, Recall, and F1-Score for a variety of diseases, including Leaf Spot, Bacterial Diseases, Fungal Diseases, Viral Diseases, Rust Diseases, Leaf Curling Diseases, or Wilting Diseases, routinely exceed 97%. These data highlight the model's outstanding ability to classify and diagnose turmeric leaf illnesses. Furthermore, the model's 99% accuracy rate emphasizes the model's suitability for realworld agricultural use. This research helps considerably to contribute to the advancement of efficient pest and disease control techniques by providing exact disease detection and classification, consequently improving crop protection and supporting sustainable turmeric production procedures. This work demonstrates the life-altering effects of advanced artificial intelligence (AI) techniques in tackling important agricultural difficulties, resulting in greater yields and improved health of turmeric crops.