Crop Yield Prediction Using Machine Learning

Abstract

The majority of India’s agricultural products have been negatively impacted by climate change in terms of performance over the past 20 years. Prior to harvest, crop output predictions would aid farmers and policymakers in deciding on the best course of action for marketing and storage. Before cultivating on the agricultural field, this project will assist the farmers in learning the yield of their crop, enabling them to make the best choices. By creating a working prototype of an interactive prediction system, it tries to find a solution. It will be put into practise to implement such a system with a user-friendly web-based graphic user interface and the machine learning algorithm. The farmer will have access to the prediction’s outcomes. So, there are various ways or algorithms for this type of data analytics in crop prediction, and we can anticipate crop production with the aid of those algorithms. It employs the random forest algorithm. There are no suitable technologies or solutions to deal with the scenario we are in, despite the analysis of all these concerns and problems, including weather, temperature, humidity, rainfall, and moisture. In India, there are numerous ways to boost agricultural economic development. Data mining can be used to forecast crop yield growth. Data mining is, in general, the process of reviewing data from various angles and distilling it into pertinent information. The most well-known and effective supervised machine learning algorithm, random forest, can perform both classification and regression tasks. It works by building a large number of decision trees during training time and producing output of the class that is the mean prediction (for regression) or mode of the classes (for classification) of the individual trees.