Cabbage Diseases Identification: A Hybrid CNN and Random Forest Approach for Multi-Classification

The study of cabbage diseases aims to mitigate their detrimental effects on crops, enhance food security, and advance agricultural research and development, rather than solely focusing on the diseases themselves. This paper uses the hybrid approach and data sizes and it was the Punjab area. This roach enhances the effectiveness of convolutional neural networks, a type of artificial intelligence model that involves a layer and a pooling layer. In this reform, various forms various recall, precision, support precision support age, and an average are used. The total accuracy achieved is t 2. The minimum accuracy in Class 6 is 0.94 and the maximum accuracy in Class 4 is 0.98. The macro average for precision is 85.98, recall is 86.47 and the F1-Score is 86.05 respectively. Micro Average for precision is 86.40, recall is 86.09 and F1-Score is 86.08 respectively. The Micro Average for precision, recall, and F1-Score is 86.09, 86.09, and 86.09 respectively. In this, is used 8 class. It is helpful for farmers because it saves money and time.