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Intelligent Integrated System for Future Crop Recommendation: Advancing Sustainable Agriculture in Cuba
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  • Jorge Felix Martínez Pazos,
  • Jorge Gulín González,
  • David Batard Lorenzo,
  • Frank Enrique James
Jorge Felix Martínez Pazos
Center for Computational Mathematics Studies. University of Informatics Science. Havana, Cuba

Corresponding Author:[email protected]

Author Profile
Jorge Gulín González
Center for Computational Mathematics Studies. University of Informatics Science. Havana, Cuba
David Batard Lorenzo
Center for Computational Mathematics Studies. University of Informatics Science. Havana, Cuba
Frank Enrique James
University of Informatics Science. Havana, Cuba

Abstract

The rise of global warming and climate change poses a potential threat to the agricultural industry. In response to this challenge, this research introduces an intelligent integrated system for recommending future crops using climate forecasting and crop recommendation models. The goal is to improve efficiency and productivity within the Cuban sector. By employing a weighted classifier with the Relative Model Accuracy Equation as the weight distribution, the classification models were improved, achieving 99.8% for each performance metric (precision, recall, and f1 score) evaluated over the test set. The main contribution of this study is an integrated intelligent system that leverages supervised machine learning techniques to predict the optimal crop for a given soil in a specific state of Cuba during a particular year among 22 possible crops. The system is built as a python module to allow its integration into future software solutions and is released under the MIT open-source license.
04 Jan 2024Submitted to TechRxiv
10 Jan 2024Published in TechRxiv