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Machine Learning-Based Optimization in Smart Agriculture for Potato Yield and CO₂ Reduction

Nizar M. SoufianSofia ArkhstanWeiguo Gee
Volume 4

Abstract

The integration of machine learning (ML) techniques into smart agriculture has significantly enhanced potato yield prediction and contributed to CO₂ emission reduction. Advanced ML models, such as Random Forest and Gradient Boosting, have demonstrated high accuracy in forecasting potato yields by analyzing diverse datasets, including soil properties, weather conditions, and remote sensing imagery citeturn search These predictive capabilities enable farmers to make informed decisions regarding fertilization and irrigation, optimizing resource use and minimizing environmental impact. Furthermore, the application of ML in precision agriculture facilitates site-specific management practices, leading to improved crop performance and reduced greenhouse gas emissions citeturn search By leveraging data driven insights, smart agriculture systems can enhance productivity while promoting environmental sustainability in potato farming.

Keywords

Machine Learning, Smart Agriculture, Potato Yield Prediction, CO₂ Reduction, Precision Farming, Sustainable Agriculture

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