Exploring Machine Learning and Ensemble Methods for Crop Yield Prediction: A Review

DOI

10.2991/978-94-6463-700-7_6How to use a DOI?

Keywords

Crop Yield Prediction; Machine Learning; Ensemble Learning; Classification methods; Supervised Learning; Unsupervised learning; Regression Methods; Bagging; Boosting

Abstract

The population growth rate all over the world, especially in countries like India, has increased the pressure on food production. Hence, there has been an increasing need for advancements in agriculture. Current technologies like data mining, machine learning (ML), remote sensing, and image analysis offer accurate ways of measuring the cultivated land and the expected agricultural harvest. By using factors like temperature, precipitation, soil type, and planted area, it is possible to develop machine learning and advanced ensemble learning models that reliably predict crop yield. This study takes a comprehensive look at the application of supervised, unsupervised, and ensemble machine learning models in agronomic yield forecasting. Studying this deeply proved that classification methods, especially those based on ensemble models, perform better than regression and unsupervised models in agricultural production forecasting. Agricultural management can benefit from these results as well, as they demonstrate how ensemble learning can be used to enhance prediction accuracy in the context of ever-increasing food requirements.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bhumika Tiwari
AU  - Navneet Kaur
AU  - Paurav Goel
PY  - 2025
DA  - 2025/04/19
TI  - Exploring Machine Learning and Ensemble Methods for Crop Yield Prediction: A Review
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 52
EP  - 62
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_6
DO  - 10.2991/978-94-6463-700-7_6
ID  - Tiwari2025
ER  -