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International Journal of Innovative Research in Technology

Toward Quantum-Enabled Precision Farming: A Hybrid Quantum Machine Learning Approach to Plant Disease Detection

Vaibhav P. Narkhede, Priyanka Narkhede

Published Jul 14, 2026• 0 citations
Publisher

Abstract

Explore the article titled Toward Quantum-Enabled Precision Farming: A Hybrid Quantum Machine Learning Approach to Plant Disease Detection from IJIRT Volume 13, Issue 2. This study evaluates the effectiveness of teaching programs on waste management knowledge among women.
DOI
10.64643/ijirtv13i2-206735-459

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This study investigates the effectiveness of a hybrid quantum-classical framework for plant disease detection, with a focus on improving classification accuracy compared to conventional approaches. Image-based datasets of crop diseases were processed and encoded into variational quantum circuits (VQCs), which were then integrated with classical machine learning classifiers. The proposed model achieved an overall classification accuracy of 95.2%, outperforming a baseline convolutional neural network that recorded 91.4% accuracy on the same dataset. Performance evaluation was carried out using multiple metrics including precision, recall, and F1-score, with the hybrid approach showing consistent gains across all categories. Furthermore, the model demonstrated reduced overfitting and improved robustness when tested under noisy and high-dimensional input conditions. These results highlight the potential of quantum-assisted machine learning to significantly enhance crop disease detection, offering a scalable and accurate pathway for sustainable agriculture and precision farming.