Cropland Quantum Learning: A Hybrid Quantum-Classical Neural Network for Cropland Classification

Accurate cropland classification is important for sustainable land management and agricultural planning. Remote sensing data, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), have proven instrumental in vegetation monitoring. Traditional machine learning and deep learning have shown remarkable results in land classification using remote sensing data. However, when facing an immense volume of data or high-dimensional features, deep learning requires large models, extensive parameters, and substantial training resources. To address the above issues, this paper proposes Cropland Quantum Learning (CQL), a quantum-classical hybrid method that utilizes quantum machine learning to extract features from geospatial information, and integrates it with a single-layer fully connected classifier to locate cultivation regions of a given target crop. We conduct comprehensive experiments to demonstrate the effectiveness of our proposed method on NDVI and EVI datasets. The results show that the proposed CQL can achieve performance comparable to traditional deep learning while significantly reducing the number of model parameters. In some datasets, even achieved better results in multiple metrics. This study provides a new solution for quantum-enhanced approaches in geospatial analysis.