Machine Learning Algorithms Aided Disease Diagnosis and Prediction of Grape Leaf

The range of diseases that can affect grape leaves has made it vital to analyze them. High-end data analytics and predictive analysis are required for a number of diseases, including black rot esca black measles, blight isariopsis, and others, in order to predict disease occurrence. For the prediction of leaf diseases, convolution neural networks combined with data augmentation have increased the degree of verification. For illness predictive analytics, a proper confusion matrix for support vector machines driven by CNN was created. Along with k-mean clustering, fuzzy logic with accurate feature extraction, and color moment definition, we also compared our results with these techniques. The findings indicate a higher effectiveness of up to 95% in correctly predicting grapes leaf disease.