Udupi: SMVITM students develop crop disease prediction with web application

Media Release

Udupi, May 23: The electronics and communication engineering department of Shri Madhwa Vadiraja Institute of Technology and Management (SMVITM), Bantakal, Udupi, has developed a web application for the prediction of crop diseases.

Agriculture, often considered the backbone of any nation, plays a crucial role in sustaining both human life and economic stability. But the modern era is beset by significant problems, chief among which is the widespread food insecurity that exists today. Crop diseases are a significant cause of food poverty and a danger to both food safety and economics. To build a strong framework for forecasting and recognizing crop diseases, to provide early diagnosis and efficient treatment for the crops, Deep Learning (DL) technology is utilized.

Final year students Rahamathunnisa, Rashi, Rimsha and Sumanth Mutalik, has developed a web application with the support from guide Chandana, assistant professor, Department of Electronics and Communication Engineering. The proposed method starts with collecting the input images of different leaves of crops like potatoes, tomatoes, peppers, etc. The dataset consists of 38 classes of both diseased and healthy crop images. The images collected from the dataset are pre-processed. Training is done in such a way that the modelmust identify unhealthy crop images. Convolutional Neural Network (CNN) algorithm is used for model building. The performance of the model was evaluated by using the classification report obtained from the model. The model has an accuracy of 97%. The result obtained is then used to build a web application. Streamlit is used to develop the web application, which is easy to use and enables the developer to build a user interface. Once the input is given to the web application in the form of an image, the input image is displayed and the disease that affected the leaf of the plant is displayed.




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Title: Udupi: SMVITM students develop crop disease prediction with web application

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