A Novel Approach for Effective Crop Production using Machine Learning

Agriculture has a big and crucial role in the growth of the country. As a result of climate change, the agricultural scientific system is coping with a host of difficulties. Machine learning (ML) is the most efficient approach to solving issues by generating good and efficient solutions. Crop yield prediction comprises calculating a crop's production based on historical data and a number of variables such as weather, soil, water, and temperature. The application of the Linear Regression approach to estimate agricultural productivity based on previous year's data is examined and defined in this project. The project's purpose is to discover a solution to the problem of cost loss. The models are created using real-world agricultural data and then evaluated on samples. The crop yield prediction model will help end-users (farmers) anticipate crop production before planting crops on agricultural land. To predict accurate results, the Linear Regression Machine algorithm is applied. The availability of a large dataset will enhance the decision-making model's improvement.