Review Paper on Prediction of Crop Disease Using IoT and Machine Learning

Abstract

Environmental parameters like humidity, temperature, rainfall, wind flow, light intensity, soil pH are main factors for precision agriculture. Fluctuations in weather parameters like humidity, temperature and so on along with the inappropriate management result into a decrease in crop productivity. Therefore disease prediction is more important to beat these problems. The real-time update will alert the farmer by indicating which crop is in trouble, so the expenses on insecticides, pesticides will reduce and overall economic condition of farmers will improve. The proposed system gives more emphasis to predict diseases of the crop with the use of the Internet of Things and machine learning algorithms. Different sensors collect the real-time data of environmental parameters like temperature, humidity, rainfall, light intensity. Utilizing these data, crop diseases are predicted using machine learning algorithms. Such predictions would warn the farmers about crop diseases through text message or web browser. This work can be extended in the future to help farmers in other ways like which fertilizer can be used to overcome this disease problem.