Analysis on Early Prediction of Cotton Plant Leaf Diseases Using CatBoost Algorithm

As the global population steadily increases, the imperative for improved crop production becomes increasingly critical to ensure food security for everyone. Addressing agricultural diseases is a primary step, requiring the identification and widespread monitoring of these ailments. To solve this problem, the proposed project introduces a robust Cotton Leaf Diseases Detection System employing advanced machine learning techniques. Leveraging the VGG16 model, the system extracts essential features from cotton leaf images and stores them in a CSV file. The Cat Boost algorithm is then applied to the datasets for training, resulting in the creation of a highly accurate model file. This model showcases excellent classification capabilities, allowing for the detection of various cotton leaves diseases. The project optimizes the accuracy and efficiency of disease detection by integrating deep learning and gradient boosting. The integration of image feature extraction and Cat Boost algorithm not only enhances the system's performance but also offers a scalable and adaptable solution for real-world applications. This pioneering approach represents a notable progression in precision agriculture, contributing significantly to the early and accurate diagnosis of cotton leaf diseases. This, in turn, contributes to more effective crop management strategies.