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Fusion of quantum computing with smart agriculture: A systematic review of methods, implementation, applications, and challenges

Sumit Kumar, Shashank Sheshar Singh, Gourav Bathla, Swati Sharma, Manisha Chahal

Published Jan 16, 2026• 5 citations
Publisher
DOI
10.1016/j.inffus.2026.104159

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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.