APP-BASED SOLUTION TO IDENTIFY & SOLVE DISEASE IN PLANTS/CROPS
Mr. Vaseegaran N1, Ms. Swathikka P2, Ms. Monika S3, Mrs. Subraja T4
1,2,3Undergraduate Student, Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India
4Assistant Professor Level 1, Department of Computer Science and Business Systems, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India
Abstract
Our project aims to develop an innovative app-based solution to identify the diseases and to solve diseases in plants/crops. With increasing challenges and difficulties to farmers at detecting crop diseases, this application will provide a user-friendly experience to solve the problems. Using some advanced technologies like Image Recognition and Machine Learning. In crop fields, due to the crop diseases, there causes a loss of irrigation and farmers get low crop production to yield foods to people. We are providing Computer Vision empowered with Machine Learning. Sine some of the diseases almost makes to look similar to farmers that make them confused, which remedy has to be used. We need better and more accurate instructions on how to use fertilisers, to correctly identify diseases, and to be able to tell apart two or more disease types that look similar when seen visually in order to avoid this situation, Convolutional neural networks are helpful in this situation. Using a foreground-based segmentation method and a two- step feature extraction technique, a novel and effective compressed sensing inbuilt plant disease detection device is created that can identify and categorise two of the main banana diseases. Real-time image collection is used to build a database for the diseases sigatoka leaf spot and banana bunchy top. It's critical to identify banana plant diseases early on to prevent significant damage to the plants. This paper offers a resource for early disease. It is suggested to use machine learning to detect banana diseases. The application also offers remedies in accordance with diseases found. Farmers can use the developed mobile application with little to no technical expertise because it is user-friendly.
Keywords : Diseases, user-friendly, Image Recognition, Machine Learning, Computer Vision, mobile application
