Agricultural Crop Yield Prediction for Indian Farmers Using Machine Learning

The growth of our country’s economy depends heavily on the sector of agriculture. Agriculture gave rise to civilization. As an agrarian country, India’s economy heavily depends on crop output. As a consequence, agriculture may be the cornerstone of every industry in our country. While organizing an agricultural enterprise, each crop must be properly selected. Several variables, the price of crops, the rate of production, and governmental regulations will affect crop selection. One of the challenging issues in the agriculture industry is the estimation of high crop output through the use of machine learning algorithms. Support Vector Regression Algorithm is used for agricultural yield prediction using various parameters. Forecasts are more heavily dependent on West Maharashtra in India. The potential for growth and a variety of production elements, such as climatic factors like rainfall and humidity, soil characteristics like macronutrients, soil type, topography, irrigation, and fertilizer management, all influence crop output. Remote and proximal sensing technologies are being used more often as a result of the requirement for timely and precise sensing of these inputs for big agricultural fields. For a better outcome, algorithms like Bagging, Random Forest, and Support Vector Regression are used to produce the best results.