Agriculture plays a very vital role in every individual life. Every living entity depends on food for its survival. Crops are the primary producers in the food cycle. If it is not able to produce a proper yield of food, then the whole food cycle is disturbed resulting in the extinction of other entities depending on each other. These days, getting a proper yield of a crop is a difficult task due to pests, insects, and unconditional weather conditions during their initial phase of growth which results in an improper or inadequate yield of crops. To detect the diseases on crops, machine learning and deep learning are being used in this paper. Some common diseases encountered by crops were stored in the database and when the farmer clicks the photo of the leaf of a crop, the model analyzes that picture and compares it with the database pictures using the concepts of artificial neural network and deep neural network thus shows the output whether the crop is affected or not and informs the farmer about the disease. Two different methods are performed for training upon the data sets (train and test data set) to decide which model will provide a better accuracy score. CNN integrated SVM and Inception V3 are used for model building and training purposes. The custom build models are saved for further use in other codes to save time and resources. The loss and accuracy graphs are being plotted for both models. The proposed model with higher accuracy is being used for performing actual predictions for image/s provided by the farmer considering the farmer knows what types of crops and their related disease can be predicted by the current model. Thus, users can take necessary precautions and get a higher yield of the crop and hence making more profit.