Okra Leaf Disease Recognition Using Synergistic Deep Learning and Ensemble Classification

The comprehensive method for accurately identifying okra leaf illnesses described in this study employs an effective blend of convolutional neural networks, also known as CNNs, and random forest (RF) classification, complemented by sophisticated image processing and machine learning approaches. this technique, which uses Convolutional Feature Mapping to extract complex features from input images, performs robust disease identification using a heterogeneous dataset of okra leaf images. Utilizing ensemble learning for accurate disease prediction, the collected characteristics are fed into a Random Forest classifier. This method performs remarkably well at spotting illnesses including Powdery Mildew, Downy Mildew, Leaf Spot, Anthracnose, Yellow Vein Mosaic Virus, and Rust. The suggested system improves decision-making in environmentally friendly agricultural operations by not only providing excellent precision but also insightful information about disease patterns. The approach offers a viable tool for redefining the control of diseases in the agricultural industry through the combination of machine learning, collaborative learning, and image processing.