Advancing Agricultural Health: Hybrid CNN-SVM Framework for Classifying Tomato Diseases

The farm tomato disease diagnosis has a key function in the improvement of agricultural productivity and food security. Historically, the diagnostic methods of diseases have mostly resulted from staining of the affected part followed by inspection and expertise of the doctor. These approaches are, therefore, often slow and prone to human errors. This work constitutes a novel model that merges Convolutional Neural Networks (CNN) syntaxes in feature extraction and Support Vector Machines (SVM) characteristics of classification that address tomato turmoil automatically into five categories. Implementing a complete dataset of tomato leaf images along with transfer learning for CNN facilitates the improvement of feature extraction and using SVM connects its strong classification capacity, which becomes even more prominent in high-dimensional spaces. The hybrid model CNN-SVM was trained and evaluated in depth with classifying 96.72% accuracy showing a huge difference from that of standalone CNN and SVM models. This research demonstrates the capacity of hybrid AI systems for fulfilling the purpose of early discovery of diseases in tomato plantations, being a versatile, correct, and scalable solution. As the results show, combining deep learning with machine learning methods must be some way of optimizing prediction accuracy and making an instrument that helps agriculturists and farmers have better crop health and bigger harvesting treaties. Future projects will be directed at enhancing the versatility of the model to accommodate not only other crop species but also the inclusion of extra data streams for highly tailored disease diagnosis.