A Hybrid CNN-SVM-Deep Learning Approach for Multi-Level Downy Mildew Disease Recognition in Spinach Leaves

In order to identify Downy Mildew infection in spinach leaves, this study used a combination of Convolutional Neural Networks (CNNs), also known as support vector machine models (SVMs). The proposed technique uses picture data to automatically identify and categorize the levels of Downy Mildew infection in spinach crops. The CNN is used for feature extraction, extracting complex structures and patterns from the leaves of spinach images, while the SVM serves as a classifier, precisely identifying the different disease severity levels. Results from experiments show that the hybrid model is successful in recognizing different degrees of Downy Mildew disease with high accuracy and precision. By providing farmers with an effective and computerized tool for early detection of diseases and crop management, the suggested method advances precision agriculture. The ultimate goal of this research is to promote sustainable agricultural methods and guarantee food security throughout the world.