Smart Farming

Abstract

Agriculture plays a vital role in the development of an agricultural country like India. As the population soars from today’s 1.3 billion to an estimated 2 billion by 2050, the demand for food is expected to more than double. In an agricultural process, the farmer aims to achieve increased yield at the least cost. The number of factors affecting the farm is high which complicates the decision-making process. The proposed system aims to assist farmers in selecting the crop for cultivation using sensor data collected from the field (Shirsath et al. in 2017 International conference on intelligent computing and control (I2C2), Coimbatore, pp 1–5 2017 [1]). Sensors are connected to the Cloud using IoT. Analytics is performed on the real-time data stored in the cloud to analyze sensor data and identify any outliers. In the cloud, Machine Learning based real-time analytics is performed to analyze sensor data and identify any outliers. This system uses Machine Learning and IoT to develop an intelligent and affordable farming product. The techniques incorporated within this system improve the precision of the result and automate crop monitoring thus reducing human involvement. Real-time data collected can be utilized to predict disease using machine learning algorithms. The real-time update will alert the farmer by indicating which crop is in trouble, so the expenses on insecticides, pesticides will reduce.