Machine Learning in Agriculture: Improving Crop Yield Prediction in India

Strategic Insights -

Researchers from the Department of Economics and Statistics have successfully applied machine learning methodologies to improve crop yield prediction in India, particularly for wheat, a staple food crop. The study used Multivariate Adaptive Regression Splines (MARS) combined with Principal Component Analysis (PCA) to analyze data from 1962-2018. The results showed that the MARS model was well-suited for predicting wheat yields in India and top wheat-producing states, with Rajasthan performing particularly well.

Key Takeaways:

  • The study used a combination of machine learning methodologies, including Multivariate Adaptive Regression Splines (MARS) and Principal Component Analysis (PCA), to improve crop yield prediction in India.
  • The MARS model was found to be well-suited for predicting wheat yields in India and top wheat-producing states, with Rajasthan performing particularly well.
  • The study analyzed data from 1962-2018, using parameters such as area under cultivation and production to predict wheat yields.
  • The performance of the MARS model was evaluated using error analyses such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²).
  • The study highlighted the importance of improved government decision-making and increased knowledge and robust forecasting among Indian farmers in various states.
  • The authors of the study included B. M. Nayana, Department of Economics and Statistics, Government of Kerala, and Kolla Rohit Kumar and Christophe Chesneau.

Statistics:

  • The study analyzed data from 1962-2018.
  • The MARS model was applied to predict wheat yields in India and top wheat-producing states.
  • Rajasthan performed particularly well in the MARS model, with a better model than other major wheat-producing states.
  • The study used Principal Component Analysis (PCA) to extract the main features from the data.
  • The study evaluated the performance of the MARS model using error analyses such as RMSE, MAE, and R².

Sources:

  • Wheat Yield Prediction in India Using Principal Component Analysis-Multivariate Adaptive Regression Splines (PCA-MARS). AgriEngineering, 2022,4(30):461-474.
  • DOI: 10.3390/agriengineering4020030 (available at https://doi-org.sdpl.idm.oclc.org/10.3390/agriengineering4020030)