Abstract:
Agriculture is the backbone of our country, which provides the livelihood for the majority of the population. In order to deal with current issues such as water shortages, supply that outpaces demand, and weather variability, farmers will need to be equipped with smart farming practices. As a result, it is predicted that there would be a decrease in crop yields as a result of changes in climatic conditions, poor irrigation facilities, reduced soil fertility, and outdated farming techniques. Machine learning is one of several ways that can be used to predict crop production in agriculture. Machine learning techniques such as Random Forests and Support Vector Regression are used, which provide more robust models than traditional linear regression models. An analysis has been undertaken in this work to examine how various machine learning algorithms may be effective in predicting agricultural yield.
