Reinventing Smart Farming Using Adaptive Quantum Machine Learning Model

Abstract

This study investigates how precision agriculture and crop production predictions may be improved using Quantum Machine Learning (QML) models, namely the Variational Quantum Circuit (VQC). The VQC outperformed traditional linear regression and other quantum models such as Quantum Neural Networks (QNN) and Quantum Convolutional Neural Networks (QCNN) by using quantum computing's ability to analyze high-dimensional agricultural data. With the lowest Mean Squared Error (MSE: 28.00), Mean Absolute Error (MAE: 3.8), and greatest R-squared (R2: 0.97), the VQC successfully identified intricate relationships between input variables such as acreage, rainfall, and fertilizer usage. Classical models, on the other hand, had more prediction errors and showed serious limits. This work opens the door for further research and the use of quantum technologies in agricultural systems by demonstrating the revolutionary potential of QML, particularly VQC, in tackling issues in agriculture, including food security, resource sustainability, and climate resilience.