An Explainable Quantum-Inspired Artificial Intelligence Framework for Sustainable Water Resource Prediction and Precision Agricultural Yield Enhancement Using Hybrid Machine Learning Techniques

Volume: 11    Issue: 2

Year of Publication: 2026

Pages: 1-8

Authors: Samuel Benny Varghese

DOI: https://doi.org/10.5281/zenodo.21245993

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Abstract

The growing need for food production and the increasing pressure on natural water resources provide major challenges for sustainable agriculture on a global scale. Climate change, unpredictable rainfall, declining soil quality, and poor water management are some of the factors that have a significant impact on agricultural productivity and environmental sustainability [1]. Even while conventional machine learning models are widely used for agricultural prediction and analysis, many existing approaches have difficulty handling complex environmental data and optimizing prediction efficiency [4]. To address these issues, this study proposes an intelligent quantum-inspired optimization and hybrid machine learning framework for agricultural output enhancement and sustainable water resource prediction within precision farming systems. The proposed method combines state-of-the-art machine learning algorithms with quantum-inspired optimization techniques to improve prediction performance and enable efficient agricultural decision-making [7]. Publicly available environmental and agricultural datasets, including information on rainfall, soil conditions, water quality, temperature, humidity, and trends in crop yield, are used in this study. Data cleaning, normalization, and feature selection are some of the preprocessing techniques used to enhance the dataset’s quality and reliability prior to model training. The developed framework incorporates multiple machine learning models, including Random Forest and Gradient Boosting, together with quantum-inspired optimization techniques to uncover hidden patterns and correlations between environmental and agricultural factors [10]. The performance of the proposed system is evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and mean squared error. Experimental results show that the hybrid quantum-inspired framework performs better than conventional machine learning methods in terms of resource optimization and prediction performance. This work contributes to the growing field of quantum-inspired artificial intelligence for sustainable agriculture and environmental management [12]. The proposed approach can help with crop monitoring, productivity forecasting, and intelligent water use in modern precision farming environments.

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Keywords

Quantum-Inspired Computing, Sustainable Agriculture, Water Resource Prediction, Machine Learning, Precision Farming, Agricultural Yield Enhancement.