An Advanced Strategy for Efficient Agricultural Production using Machine Learning

Agriculture is very important to the prosperity of the country. Climate change has created a variety of problems for the agricultural academic system. Since machine learning (ML) provides effective and affordable solutions, it is the most effective method for solving problems. Crop yield prediction involves estimating the crop yield based on historical data and a variety of other factors, including the climate, sometimes the environment, the water source, and warmth. This study analyses and discusses the use of the linear regression technique for evaluating agricultural production based on data from the earlier study. This study uses geographic and meteorological data to anticipate winter wheat production. It is motivated by this data science issue. The project’s goal is to find a solution to the cost loss issue. Actual agricultural data is used to build the models, which are then evaluated on samples. Using the vegetable yield forecasting model, end users (agriculturalist) may foresee crop production as they plant crops on agricultural land. The Linear Regression Algorithm method is used to forecast results with accuracy. The decision-making model’s creation will be aided by the availability of a sizable dataset.