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Elsevier eBooks

AgriQML: A novel quantum machine learning framework for agriculture crop yield prediction

Deepa Bhadan, Thanjaivadivel M, R Lakshmana Kumar

Published Aug 14, 2026• 0 citations
Publisher
DOI
10.1016/b978-0-443-44247-6.00006-3

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This paper introduces a hybrid machine learning and quantum, inspired framework for the prediction of crop yield and profit in precision agriculture. The presented system combines classical machine learning models with quantum, inspired learning such that the model's predictability and generalization can be increased. Random Forest regressors are employed to estimate crop yield and economic profit, thus providing a stable and reliable baseline for the performance. Quantum Kernel Ridge Regression and a Variational Quantum Circuit have been added to this framework as corrective components to enhance the model's generalization. The proposed technique involves methodical data preprocessing: categorical attributes are encoded (soil type, season, crop name, and disease status), and numerical features are scaled (rainfall, nutrient level, fertilizer usage, and cultivated area). Instead of using image, based disease detection methods, disease condition is considered as a structured input feature and is merged with the yield and profit estimation process. The quantum-inspired module, which is implemented with Qiskit, employs Principal Component Analysis for reducing dimensions and utilizes a ZZFeatureMap to encode quantum features to construct quantum kernels. By providing an effective framework for data-driven agricultural decision support, the current system is deployed via an interactive Streamlit application for dataset uploading, model training and prediction, and visual analysis through correlation heatmaps and yield-profit trend plots. Experimental results have shown that a hybrid integration of classical machine learning and quantum- inspired methodology could enhance predictability, interpretability, and practical usability, thus offering this framework suitable for data-driven agricultural decision support.
Smart agriculture has emerged as a transformative approach to enhance crop productivity, resource efficiency, and environmental sustainability using advanced sensing and computational technologies. Wireless Sensor Networks (WSNs) enable real-time monitoring of agricultural parameters such as soil moisture, temperature, humidity, and crop health. However, traditional machine learning models struggle to efficiently process high-dimensional, noisy, and heterogeneous agricultural sensor data. This study proposes a Wireless Sensor-Driven Smart Agriculture framework using Scalable Quantum Convolutional Models (SQCM). The proposed model integrates wireless sensor data acquisition with quantum-inspired convolutional neural networks to enhance pattern recognition, predictive accuracy, and scalability. The quantum convolutional layers enable high-dimensional feature representation, while classical layers ensure efficient decision-making for agricultural optimization. The model is evaluated using agricultural sensor datasets, and performance is measured using accuracy, precision, recall, and RMSE for environmental prediction tasks. Experimental results demonstrate that the proposed quantum-enhanced architecture outperforms traditional machine learning and deep learning models in crop condition prediction and resource optimization. The framework is suitable for precision agriculture, automated irrigation systems, and smart farming applications.