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Leveraging Quantum Machine Learning for Precision Nutrient Management in Sustainable Crop Production
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Author(s): Soumi Ghosh (Maharaja Agrasen Institute of Technology, India)and Ritik Raj (Maharaja Agrasen Institute of Technology, India)
Copyright: 2026
Pages: 40
Source title:
Revolutionizing Sustainable Food Production With Quantum Computing
Source Author(s)/Editor(s): Mong Fong Horng (National Kaohsiung University of Science and Technology, Taiwan), Osamah Ibrahim Khalaf (Al-Nahrain University, Iraq), Chin-Shiuh Shieh (National Kaohsiung University of Science and Technology, Taiwan)and Vishal Jain (School of Engineering and Technology, Vivekananda Institute of Professional Studies, New Delhi, India)
DOI: 10.4018/979-8-3373-3957-3.ch004
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Abstract
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.
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