International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Hybrid Quantum–Classical Machine Learning Framework for Village-Level Soil Environmental Sustainability and Land Degradation Risk Assessment in Bilaspur District,India

Author(s) Mr. Chandrashekhar Chandrashekhar, Dr. Amit Kumar Chandanan
Country India
Abstract The presence of multivariate dependency relationships, spatial heterogeneity, and nonlinear nutrient interactions makes it challenging to evaluate the environmental sustainability of soils accurately using conventional statistical models. For the Bilaspur district in the state of Chhattisgarh, India, this research proposes a hybrid quantum-classical machine learning approach for land degradation risk classification and soil environmental sustainability.
A structured dataset of 677 soil samples from 17 villages spread across four administrative blocks was analyzed using twelve micronutrient and physicochemical parameters (pH, EC, OC, N, P, K, S, Zn, Fe, Mn, Cu, and B). To translate standardized nutrient variables into understandable sustainability descriptors, composite environmental indicators such as the Soil Nutrient Balance Index (SNBI), Soil Fertility Index (SFI), Micronutrient Deficiency Index (MDI), Soil Degradation Index (SDI), and Environmental Sustainability Index (ESI) were created mathematically. After removing scale dominance effects with Min–Max normalization, deep autoencoder-based nonlinear dimensional compression (5→16→8→4 architecture) was performed using Mean Squared Error optimization and ReLU activation.
After being extracted, the 4-dimensional latent representation was embedded into a 4-qubit ZZFeatureMap and rescaled to the angular domain [0, π]. Sustainability risk classification was carried out by a precomputed Quantum Support Vector Machine (QSVM) using ESI quantile thresholds, and quantum state overlap was calculated using a fidelity-based Quantum Kernel Estimation (QKE) framework. With a balanced distribution of classes, the suggested model's accuracy was 88.23%.
The findings show that quantum kernel learning successfully captures nonlinear environmental sustainability structures, offering a scalable decision-support tool for sustainable land-use planning, region-specific soil management, and precision agriculture.
Keywords Soil Environmental Sustainability, Quantum Machine Learning (QML), Quantum Support Vector Machine (QSVM), Precision Agriculture, Soil Nutrient Imbalance, Environmental Sustainability Index (ESI), Bilaspur District Soil Study.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-31
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.72858
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E-ISSN 2582-2160
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