The objective of the proposed project is to develop a product which detects crop disease even from a remote area. As of now, due to lack of proper knowledge, farmers in remote places face a lot of problems in early detection of plant diseases such as Powdery Mildew, Rusts etc which go unnoticed most of the time and cause severe problems. Herein we are planning to integrate Machine Learning and IoT to arrive at a solution for the identified problem and achieve our goal.
Crop diseases are generally caused by pests, insects, pathogens, and have an adverse effect on the yield of the crop, amounting towards decrease in productivity of the crop. Farmers across the country are facing severe losses due to various crop diseases, and one of the main reasons preventing them from arriving at a solution is not being able to detect the disease at an early stage. To overcome this problem, we are proposing a Crop disease detect model using Machine Learning and IoT.Farmers residing in remote places do not have the necessary resources/ and facilities so that they can consistently identify the disease in its early stage. The proposed project endeavours towards developing a product which reads a crop image and sends the image to cloud storage wherein an appropriate Machine Learning model is deployed for detection of disease. Results of the process will be sent back to the product, which then will be displayed to the farmer.
- Early detection of grapes diseases using machine learning and IoT
- Machine learning regression technique for cotton leaf disease detection and controlling using IoT
- Disease Prediction of Mango Crop Using Machine Learning and IoT
Note: The complete(paper) pdfs can be found in References folder.