Study on Machine-Learning Algorithms in Crop Yield Predictions specific to Indian Agricultural Contexts
Suresh Kumar Sharma, Durga Prasad Sharma, Jitendra Kumar Verma
Abstract
Prior and well-grounded produces evaluation is vital in quantifying a well and financial assessment at the field level for discovering agricultural commodity strategic action plans for import-export policies and increasing farmer incomes. Crop production projections are performed utilizing machine learning algorithms to estimate a higher crop yield, which is one of the most difficult challenges in the agriculture business. Because of the growing importance of agricultural yield prediction, this article takes an in-depth look at how Machine Learning (ML) approaches may be utilized to forecast crop production. The present state of agricultural yield worldwide is discussed first, followed by a brief introduction of extensively utilized features and forecasting procedures. Forecasting crop yields is a serious issue in agriculture, plus there is a large dataset that makes it arduous for farmers to select seeds and forecast yields. In today’s circumstances, since the extension in population, agricultural production must be raised simultaneously to fulfill people’s wants. This paper is a detailed study of various aspects of crop yielding in India using machine learning techniques and artificial intelligence.