Grape leaf disease prediction using various machine learning techniques: - A technical review

Grape leaf infection analysis has become important part due to its variety of nature to catch disease. There are various diseases like black rot esca black measles, blight isariopsis, etc which need high end data analytics and predictive analysis for carrying out disease prediction. Convolution neural network integrated with data augmentation has brought a greater degree of verification for leaf diseases prediction. Proper confusion matrix was generated for support vector machine powered with CNN for disease predictive analytics. Results compared with k-mean clustering, fuzzy logics with proper feature extraction and color moment definition. The results show a higher efficiency of up to 95% in accurate grapes leaf disease prediction.