Machine learning for IoT-based smart farming

Abstract

Agriculture balances food requirements for mankind, and the supply of essential raw materials for many industries is the fundamental occupation in India. Smart farming allows analyzing the growth of crops and the parameters which influence crop growth and supports farmers in their activities, it is more profitable and reduces irrigation wastages. The proposed model is a smart farming system that analyzes the influence of parameters on crop growth and predicts the soil condition using a machine learning algorithm. Temperature, Ph, humidity, gas, and water level are the few most essential parameters to determine the quantity of water required and to find hazardous gas in any agriculture field. This system comprises temperature, pH, humidity, smoke detector, and water level sensor, deployed in an agricultural field, sends data through a microprocessor, developing an IoT device with cloud. In this study, we present a model that predicts soil series with regard to land type and, in accordance with the prediction, suggests appropriate crops. For soil land classification and crop prediction application is developed using KNN algorithms. Three steps are necessary for its implementation: the first is data collecting using sensors placed in an agricultural field, the second is data cleaning and storage, and the third is predictive processing utilizing the ML technique. The results obtained through the algorithms are sent to the cloud, which helps in decision-making in advance.