Crop Yield Prediction Using ML

Abstract

India’s agriculture sector is pivotal to the nation’s
economy and sustains livelihoods for millions. With diverse agroclimatic zones, India boasts a rich agricultural heritage encompassing crops like rice, wheat, sugarcane, and cotton.For farmers,
decision-makers, and other stakeholders to allocate resources and
ensure food security, accurate crop yield prediction is essential.
This study looks into how machine learning algorithms might be
used to increase the precision of crop yield forecasts in India.The
study looks at how machine learning models can take into account
a number of variables that impact crop yields, such as crop
type, season, state, area, fertilizer, pesticide, and rainfall. The
effectiveness of various algorithms, such as LinearRegression,
Lasso, Ridge and DecisionTreeRegressor, is evaluated.Out of
the three Machine Learning methods, the DecisionTreeRegressor
algorithm demonstrated the best performance, as seen by its
lowest MAE (mean absolute error) value and highest R² value.
These findings imply that machine learning algorithms have
the potential to greatly increase agricultural yield projections’
accuracy in Morocco, which might enhance food security and
maximize farmers’ use of available resources.