The proposed system uses in identification of plant disease and provides remedies which will be used as a defence reaction against the disease. The data obtained from the Internet is correctly segregated and therefore the totally different plant species are identified and are renamed to form a proper database then obtain a test-database that consists of varied plant diseases that are used for checking the accuracy and confidence level of the project. By using training data we will train our image classifier and then output will be predicted with more accuracy. We use Convolution Neural Network(CNN) which comprises different layers which are used for prediction. A image drone model is additionally designed which might be used for live coverage of huge agricultural fields so that a high-resolution camera is connected and can capture images of the plants which will act as input for the software, based on which the software will tell us whether the plant is healthy or not. With our code and training model we have achieved an accuracy level of 78% .Our software gives us the name of the plant species with its confidence level and also the remedy that can be taken as a cure. Deep learning has become prominent with big data technologies and high-performance computing to create new opportunities for data-intensive science. In this paper, we tend to give a comprehensive review of analysis applications of deep learning in agricultural systems. The prediction and diagnosis in this project demonstrate how agriculture will benefit from deep learning technologies. The deep learning techniques used for farm management systems are entering into real-time artificial intelligence.