Abstract

Smart agriculture is very broad are of research using deep learning techniques. This can help to predict the different disease, crop status, soil status, water quality prediction etc. this study is about implementation of VGG-16 model on image dataset of cotton crop diseases data. The implementation is performed using python language in Jupiter notebook tool. The deep learning library named tensor flow along with basic ML libraries are used for the experimentation. The model accuracy, precision, recall, F1 score parameters are showing very positive results. The model shows training accuracy shows up to 0.98 whereas testing accuracy is up to 0.97 running on 30 epochs as discussed parameters. The model shows high accuracy which is acceptable for the better recommendation for the crop data image classification.