Prediction of pesticides and identification of diseases in fruits using Support Vector Machine (SVM) and IoT

Fruit diseases are the cause of crop destruction and economic losses in agriculture fields.By the use of fertilizers, insecticides, pesticides of higher level have greatly in increase of the side effects in humans because of the incontrollable level of pesticides in those fruits or vegetables so we have to develop a suitable solution to identify diseases and pesticides on which human consumption.It has been experimentally tested for disease detection and classification three stages make up the processing- based considered approach required for image segmentation, the first stage is RGB to grey conversion, followed by median filtering, edge detection, and morphological operations. In the case of a second-degree output feature both domains are compared for feature extraction and a third-step image separated using a separate kernel on a vector support machine. SVM algorithm is selected and the image of the fruit is obtained by them. This Process is applied to a fruit where an infected fruit is found and stored later on a cloud server MATLABthingspeak.