Data Analytics Framework for Crop Yield Prediction Based on the Impact of Climate Using a Hybrid Model

In the modern Indian economy, agriculture is the most vital sector. Agriculture is an important source of revenue for the majority of the population of the world. Proper management of crops, crop species, crop field conditions, livestock, etc., can improve agricultural systems. The four primary subcategories of crop management are crop yield production management, weed management, crop disease management, and crop quality management. Planning agriculture, utilizing resources, and satisfying demand to ensure food security depend on accurate crop yield estimation. Crop yields are heavily influenced by climatic features like solar radiation, wind speed, rainfall, temperature, etc. Traditionally, crop yield management was completely performed by the farmers based on their experience using various techniques like crop rotation, mixed cropping, timely growing of crops, etc. These methods are combined with modern technologies like Artificial Intelligence, Internet of Things, drones, precision agriculture, etc., to improve crop productivity while considering preservation of natural resources. This study performed crop yield prediction using four baseline models based on machine learning: Decision Tree, Multiple Linear Regression, Support Vector Regression, and Random Forest. One of the best baseline models is further improved using nature-inspired algorithms. Four nature-inspired optimization algorithms used are: Whale Optimization Algorithm, Harris Hawks Optimization, Particle Swarm Optimization, and Grey Wolf Optimization. Particle Swarm Optimization-based optimized RF performed the best with R^2=98.32, MAE=9.2766.

@article{Kaur2026,
  title     = {Data Analytics Framework for Crop Yield Prediction Based on the Impact of Climate Using a Hybrid Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {9},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Narinder Kaur and Vishal Gupta},
  doi       = {10.14569/IJACSA.2026.0170957},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170957}
}