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Lecture notes in networks and systems

Harnessing Quantum Machine Learning Techniques for Enhancing Crop Productivity

S Sathya, Madhumathi Ramasamy

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-981-96-9048-0_42

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This paper presents a novel approach to crop disease classification through the integration of quantum computing principles with deep learning techniques. We introduce a hybrid quantum neural network architecture that leverages quantum mechanical phenomena, including superposition and entanglement, to enhance crop disease detection capabilities. Using a comprehensive dataset of 23,265 images spanning 19 disease classes across five major crop species, our model achieved a test accuracy of 0.7958 with a weighted average F1-score of 0.85. The architecture combines classical convolutional layers for initial feature extraction with quantum processing units for advanced pattern recognition, demonstrating significant potential in the diagnosis of agricultural diseases. Our findings establish the viability of quantum-enhanced neural networks in agricultural applications, while highlighting pathways for future development in quantum machine learning systems.
Contemporary agriculture is having major difficulties in assuring nutrient delivery and caring for the environment. The paper proposes integrating quantum machine learning (QML) techniques with precision agriculture tools to bring improvements to nutrient management. Predicting the correct amount, timing and distribution of fertilizer works much better with our new quantum-based strategy than with established ones. Analyzing the interactions among soil, plants and the atmosphere with quantum variational circuits and quantum kernel methods allows us to predict how much fertilizer is needed with an accuracy of 94.7% and helps lower waste by 38.2%. With QAOA implementation, real-time adjustments can be done in variable-rate systems. This type of research aims to overcome important problems with current algorithms unable to work with high-dimensional datasets in agriculture. This work creates the base for new agriculture systems that help achieve high crop yields while keeping the environment safe through quick and effective methods of optimization.