Crop Yield Prediction Using Machine Learning: A Pragmatic Approach

Abstract The agricultural sector is the major driver of revenues in India. organic , economic, and seasonal factors all have got a bearing on an agricultural producer's manufacturing. Accurate crop yield prediction (CYP) is required due to the agricultural indus-try's rapid innovation and liberalized market economy. Accurate prediction is greatly aided by the chosen characteristics and machine learning (ML) techniques Any ML Algorithm’s performance may be enhanced by using a unique set of features from the same training dataset. This study assesses the key characteristics of an accurate crop yield prediction. For greater accuracy, it uses machine learning (ML) methods like Random Forest (RF), Adaboost, Gradient Boost, and Support Vector Machine (SVM). The agriculture dataset has 2201 instances in it. 80% of them are randomly chosen for themodeìs training, while 20% are used to test themodeìs predictive power. The results show that the Random Forest approach gets the highest level of accuracy. The goal of this paper is to predict crop yields. Using different Machine Learning Algorithms so that farmers can make their yields higher.