Iot Based Dynamic Operations to Automate the Machinery Tools for Agriculture

Abstract

Abstract— Climate change has had a negative impact on the performance of the huge percentage of India's crops over the past 20 years. Prior to harvest, crop yield predictions and fertilizer prediction would aid farmers and policymakers in deciding on the best course of action for marketing and storage. Before cultivating on an agricultural field, this project will help farmers determine the yield of their crop and will also suggest the right fertilizer for the crops, assisting them in making the right choices. It makes an attempt to solve the problem by creating a prototype of an interactive prediction system. The dataset is collected from the IOT sensors, data like soil moisture, temperature, humidity, soil type, NPK. The collected dataset is processed using data analytics technique. The cleaned and pre-processed data are trained on machine learning algorithms. The machine learning algorithm helps to analyse the crop yield for the next sowing and also suggest the fertilizer for the better yield. An easy-to-use web-based graphic user interface will be implemented in such a system as the farmer will be informed of the outcome of the prediction. As a result, there are numerous methods or algorithms for big data analysis in  predict yield and fertilizer prediction, and with the aid of those algorithms, one can predict crop yield and suggest fertilizer using algorithms like the Random Forest algorithm(RF) and SVM.