Quantum-Enhanced Soil Fertility Prediction Using Quantum-Inspired Feature Embedding and Quantum-Enhanced Random Forest (QRF)

Project Code :TCMAPY2469

Objective

This project aims to develop an intelligent soil fertility prediction system using quantum-inspired machine learning techniques. The system applies Quantum-Inspired Feature Embedding along with Quantum-Enhanced Random Forest (QRF) algorithms to improve prediction accuracy and feature representation of agricultural soil data. By analyzing soil nutrients, environmental conditions, and fertility indicators, the model helps farmers and agricultural experts make data-driven decisions for crop planning and soil management. The project enhances agricultural productivity, supports sustainable farming practices, and demonstrates the application of quantum-inspired AI in precision agriculture.

Abstract

Soil fertility assessment plays a vital role in precision agriculture, helping farmers make informed decisions related to crop selection, nutrient management, and sustainable resource utilization. However, conventional soil testing methods, although reliable, are often time-consuming, labor-intensive, and costly. To address these challenges, this project introduces a machine learning–based soil fertility prediction framework enhanced with quantum-inspired computational techniques. The system analyzes key soil attributes—including pH, electrical conductivity, organic matter, and essential nutrient concentrations—to predict soil fertility with improved accuracy and efficiency. A quantum-inspired methodology is employed using cosine–sine–based single-qubit encoding and quantum entanglement modeling to convert classical soil features into high-dimensional quantum states. This transformation captures deeper, non-linear interactions within the soil data, enabling more robust classification than traditional feature representations.

Experimental evaluation demonstrates that the Quantum-Enhanced Random Forest (QRF) model achieves superior accuracy, stability, and generalization compared to classical approaches. The integration of quantum feature embedding strengthens decision boundaries, reduces the influence of noisy inputs, and enhances the model’s capability to interpret complex soil patterns. The results highlight the significant potential of hybrid quantum–machine learning techniques in the agricultural domain, offering faster computation and higher predictive reliability. This work contributes to the emerging field of quantum agriculture and emphasizes its relevance in advancing sustainable, data-driven farming workflows for modern agriculture.

Keywords: Soil Fertility, Quantum Machine Learning, Quantum-Enhanced Random Forest, Quantum Encoding, Precision Agriculture, Feature Entanglement, Hybrid Quantum Models.

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