Published 2026-05-27
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Copyright (c) 2026 Swami Vivekananda University Press
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Abstract
Agriculture is the first and foremost factor for survival. Worldwide, agriculture holds the major responsibility for improving the economic contribution of the nation. However, still the most agricultural fields are underdeveloped due to the lack of deployment of ecosystem control technologies. Due to these problems, the crop production is not improved which affects the agriculture economy . To prevent this problem, Agricultural sectors have to predict the crop from a given dataset using machine learning techniques.
Backbone of the Indian Economy is agriculture as more than 50% Indian’s are farmers. In the economic sector agriculture plays a vital role. Day by day the population is increasing on a large scale which increases the demand for food. The early methods used by farmers are not sufficient enough to fully fill today’s requirement, thus new methods are invented which in return brings employment for people. Machine learning Technology in agriculture has helped humans a lot such as identifying particular climate for particular crops similarly, it’s soil type, pH value and water supply to the crop.
The role of machine learning comes in this place. As it has the decision making property ML can be real-world solutions for crop yield prediction. The predictions made by ML algorithm will help the farmers to decide which crop to grow to induce the most yields considering various environmental factors.
The present research focuses on predicting the yield of crops by applying various ML techniques and providing a detailed analysis in terms of accuracy. The classifier model used here is Logistics Regression, Native Bayes and Random Forest , Decision Trees , Linear SVM, K-means clustering.