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Quantum Computing for Smart Agriculture Optimization in Potato Farming and CO₂ Emission Reduction

Sam M. K.Weiguo GeeSofia Arkhstan
Volume 4

Abstract

The integration of quantum computing into smart agriculture offers novel solutions for optimizing farming practices and addressing environmental challenges. This study explores the application of quantum computing to optimize potato farming while simultaneously reducing CO₂ emissions. Quantum algorithms are used to model complex agricultural processes, including soil conditions, crop yield predictions, and water usage, allowing for more accurate and efficient resource management. By leveraging quantum-enhanced machine learning techniques, the system identifies optimal farming practices that minimize input costs, enhance crop yields, and reduce carbon footprints. The study demonstrates that quantum computing can significantly improve decisionmaking in smart agriculture, promoting sustainability and CO₂ reduction in agricultural systems.

Keywords

Quantum computing, smart agriculture, potato farming, CO₂ emission reduction, optimization, sustainability

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