NeuraLeaf: Unleashing the Power of CNN-SVM Fusion in Weed Disease Classification

This study employs a sophisticated framework for the classification of weed diseases based on the integration of support vector machine (SVM) and convolutional neural network (CNN) models. The model shows high accuracy, recall, and F1 scores across multiple disease categories using a large dataset comprising various weed diseases. The model’s ability to reduce the number of false positives also comes out clearly with high accuracy values like 93.64% for Verticillium wilt and 93.36% for Downy Mildew. Also, remember numbers that are astonishing like they show the model captured incidences for given diseases: 94.79% for Leaf Spot and 91.99% for Verticillium Wilt. Measures’ seamless integration across all classes demonstrates the robustness, and reliability of the model for the complex field "weed disease classification". Furthermore, an overall accuracy rate of 98% also supports that the model has continued to improve across all types of diseases. This study also authenticates the CNN-SVM model as a reliable instrument in precise and reliable disease weed.