Assessing Environmental Impact: Machine Learning for Crop Yield Prediction

¹ Department of Computer Science, Gokaraju Rangaraju Institute of Engineering and Technology, India.
² The Islamic University, Najaf, Telangana, Iraq

Abstract

Most agricultural crops have been badly affected by the effect of global climate change in India. In terms of their output over the past 20 years. It will allow policymakers and farmers to take effective marketing and storage steps to predict crop yields earlier in their harvest. This project will allow farmers to capture the yield of their crops before cultivation in the field of agriculture and thus help them make the necessary decisions. Implementation of such a method with web-based graphic software that is simple to use and the machine learning algorithm can then be distributed. This paper focuses mainly on predicting the yield of the crop by applying various machine-learning techniques. The classifier models used here include KNN, Decision Tree, Random Forest, and Voting Classifier. The prediction made by machine learning algorithms will help the farmers decide which crop to grow to induce the most yield by considering factors like temperature, rainfall, humidity, pH, etc. This bridges the gap between technology and the agriculture sector.