Quantum Computing Methods for Agricultural Applications: A Systematic Review Protocol
Abstract
Precision agriculture generates increasingly complex datasets from sensors, satellite imagery, and IoT devices, creating computational challenges for conventional machine learning and optimization approaches. Quantum computing has emerged as a potential alternative for addressing high-dimensional and nonlinear problems through quantum algorithms. Although initial applications have been reported in areas such as crop classification, prediction, and resource optimization, a systematic assessment of these approaches in agriculture is still lacking. This review aims to synthesize current applications, identify methodological trends and limitations, and highlight future research opportunities in quantum computing for agriculture.
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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