Machine Learning Prediction Analysis using IoT for Smart Farming

Abstract

Commonly, the implementation of agricultural practices (e.g., ploughing, sowing, watering, pests control, and harvesting.) purely depends on climate change, recommendations from previously experienced rules, and Governmental policies. For fulfilling Term smart farming, i), we employed real-time applications over sensors to capture climate changes of soil and atmosphere. ii) we defined agriculture practice rules by applying machine learning techniques over the last five years data iii) By federations of real-time data from the field sensors and rules, we define the time for implementation of the practice. This federation eliminates many malfunctions in old ways of smart farming for precision agriculture.