MACHINE LEARNING APPROACHES FOR RELIABLE CROP YIELD FORECASTING

Special Issue - Innovative Commerce: Bridging Business and Computer Applications (ICBBCA-2026) |PG Department of Commerce with Computer Applications, Mannar Thirumalai Naicker College, Madurai – March 2026| International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Agriculture plays an important role in ensuring food security and economic development. Predicting crop yield accurately helps farmers and policymakers make better decisions related to crop planning and resource management. Traditional prediction methods are often based on historical averages and may not capture complex environmental relationships. This study focuses on predicting crop yield using machine learning techniques based on parameters such as rainfall, temperature, soil type, and humidity. Machine learning algorithms such as Linear Regression, Decision Tree, and Random Forest are used to analyze agricultural data and generate yield predictions. The results show that machine learning models can improve prediction accuracy and support better agricultural decision-making.

References

1. R. Medar, V. Rajpurohit, and S. Shweta, “Crop Yield Prediction Using Machine Learning Techniques,” International Journal of Computer Applications, 2016.
2. N. Chandrasena Reddy et al., “Crop Yield Prediction Using Machine Learning,” International Journal of Engineering Research & Technology, 2023.
3. S. Jeong et al., “Random Forests for Global and Regional Crop Yield Predictions,” PLOS ONE, 2016.
4. K. Shankar and S. Kumar, “Machine Learning Approaches for Agricultural Yield Prediction,” Journal of Agricultural Informatics, 2019.
5. J. Liakos et al., “Machine Learning in Agriculture: A Review,” Sensors Journal, 2018.

 

Keywords

Crop Yield Prediction, Machine Learning, Agriculture Data Analysis, Random Forest, Decision Tree, Linear Regression, Smart Agriculture.