A Novel Hybrid Method for Predicting Specific Crop Yield

B.Rebecca; P.Siva Padmini; V.Dhanakodi; M.Gayathri

doi:10.58599/IJSMCSE.2024.1109

Authors

B.Rebecca Department of Computer Science and Engineering, Marri Laxman Reddy Institute of Technology and Management, Dundigal, Hyderabad, Telangana, India
P.Siva Padmini Department of Computer Science and Engineering-Data Science, Marri Laxman Reddy Institute of Technology and Management, Dundigal, Hyderabad, Telangana, India
V.Dhanakodi Department of Computer Science and Engineering, Mahendra College of Engineering, Minnampalli, Tamil Nadu, India
M.Gayathri Department of Computer Science and Engineering, Mahendra College of Engineering, Minnampalli, Tamil Nadu, India

DOI:

https://doi.org/10.58599/IJSMCSE.2024.1109

Keywords:

Machine learning, Hybrid method, Ensemble learning, Satellite imagery, Agricultural management, Crop yield prediction

Abstract

Predicting crop yields is crucial for economic planning, food security, and agricultural management. Conventional approaches frequently need accurate results regarding the complex interaction between environmental conditions and crop-specific traits. By combining the best features of machine learning algorithms with expert-level domain knowledge, we provide a new hybrid approach to predicting crop yields that is both state- and crop-specific. Our approach integrates historical yield data, satellite imagery, meteorological data, soil properties, and crop-specific information to build robust predictive models. Specifically, we employ ensemble learning techniques, including random forest and gradient boosting, to capture complex nonlinear relationships and improve prediction accuracy. Additionally, we incorporate domain knowledge through feature engineering and selection to enhance model interpretability and generalization. We validate our method using real-world datasets from diverse geographical regions and crops, demonstrating its superior performance compared to traditional approaches. Our proposed hybrid method offers a promising solution for accurate and reliable crop yield prediction, facilitating informed agricultural decision-making.

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