Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)

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Quantum-Enhanced Computational Models for Multi-Variable Optimization in Precision Agriculture

Authors
Daya Shankar Verma1, *, Mrinal Dafadar2, Jitendra K. Mishra3, Ankit Kumar4, Manish Kumar Priydarshi5
1Department of Computer Science and Engineering, Indian Institute of Information Technology Ranchi, Ranchi, Jharkhand, 835217, India
2Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India
3Department of Electronics and Communication Engineering, Indian Institute of Information Technology Ranchi, Ranchi, Jharkhand, 835217, India
4Department of Information Technology, Guru GhasidasVishwavidyalaya, Bilaspur, India
5Center for Nano-Chemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
*Corresponding author. Email: dsverma.rs@iiitranchi.ac.in
Corresponding Author
Daya Shankar Verma
Available Online 17 July 2025.
DOI
10.2991/978-94-6463-787-8_34How to use a DOI?
Keywords
Quantum-Enhanced Computational Models; Hybrid Quantum Optimization and Machine Learning Framework (HQOMLF)
Abstract

Precision agriculture is experiencing a technological transformation driven by artificial intelligence (AI), the Internet of Things (IoT), and big data analytics. Nonetheless, traditional computation models find it difficult to handle large-scale high-dimensional datasets in agriculture. By utilizing phenomena like superposition and entanglement, quantum computing represents a fundamental departure that can greatly accelerate and optimize such algorithms. This paper presents the Hybrid Quantum Optimization and Machine Learning Framework (HQOMLF), which comprises its derivatives, such as the Quantum-Enhanced Resource Allocation (QERA) and Quantum Convolutional Neural Networks (QCNN) for effective resource distribution and predictive analytics. The proposed method outperforms classical models with respect to computation speed, accuracy, and scalability. It demonstrates via experiments that processing speed can be improved up to 3.5 × and resource optimization of SBT can be enhanced by 11.3% using the proposed method. Based on data analysis, there are several unsolved problems, like hardware limitations and complexity of the algorithm, but quantum computing in agronomics has well-picking potential. These results set the stage for future quantum-classical hybrid models towards sustaining, data-driven agricultural solutions.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
Series
Advances in Intelligent Systems Research
Publication Date
17 July 2025
ISBN
978-94-6463-787-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-787-8_34How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Daya Shankar Verma
AU  - Mrinal Dafadar
AU  - Jitendra K. Mishra
AU  - Ankit Kumar
AU  - Manish Kumar Priydarshi
PY  - 2025
DA  - 2025/07/17
TI  - Quantum-Enhanced Computational Models for Multi-Variable Optimization in Precision Agriculture
BT  - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
PB  - Atlantis Press
SP  - 437
EP  - 451
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-787-8_34
DO  - 10.2991/978-94-6463-787-8_34
ID  - Verma2025
ER  -