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Mr. Rohit Ghumare, Mr. Abhishek Budar, Mr. Rohan Bordekar, Prof. Nusrat Parveen


Agriculture is the mainstay of the Indian economy. Almost 70% of people depend on it and share a major part of the Gross Domestic Product (GDP). The economy of our country is heavily dependent on agriculture and if it is affected then it will have a major impact on the economy. Diseases happening on the crops are mainly on the leaves which affects the reduction of both quality and quantity of agricultural products. If the farmers completely rely on pure nakedeye observation of experts to detect and classify such diseases, it can prove to be very expensive and is not completely accurate. The improvement in the field of Artificial Intelligence can be proved useful towards finding a solution for diseases happening to crops. The study aims to design an application for automatic detection and classification of diseases happening to crops. Also, the application should provide fast, cheap, and accurate solutions for the task which can be of great realistic significance. The system will be built by integrating several intelligent technologies like Image processing and Convolutional Neural Network (CNN). The CNN model will be built using TensorFlow library Keras by defining convolutional layers which converts an image into 2D Convolve which further by using feature_extractor_layer gives 95% accuracy. The CNN model will be mainly focused on increasing the processing speed. Taking althese things into consideration we aim at building a system that identifies the disease that has affected the crop and predicts some pesticides to cure the disease.

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