Optimizing Crop Yield Forecasts Using Quantum Machine Learning Techniques with High-Dimensional Soil and Weather Data - Paper Focus and Method Summary

Aug 5, 2026

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This paper examines quantum machine learning for improving crop yield prediction accuracy using multifeature soil and climate data. The work targets the inefficiencies of classical supervised approaches caused by complex nonlinear relationships and high dimensionality across multiple years. QSVM and QNN are integrated into conventional machine learning pipelines to learn from large, highly complex datasets. Results indicate quantum-enhanced models achieve stronger predictive power with improved MSE and robustness, supporting practical agricultural decision-making.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Challenges in crop yield forecasting

High-dimensional soil and weather data

Proposed quantum machine learning approach

QSVM and QNN integration

Experimental results and implications

Predictive performance and robustness

Optimizing Crop Yield Forecasts Using Quantum Machine Learning Techniques with High-Dimensional Soil and Weather Data Jamshaid Basit1 , Hira Arshad1, Amna Bibi1 1 National University of Sciences and Technology, Islamabad | ARTICLE INFO | ABSTRACT | | --- | --- | | Article History:
Received: August 11, 2024 | This paper focuses solely on the possibility of applying quantum machine learning methods to increase crop yield prediction accuracy based on multifeature soil and climate data. The main goal is to increase the efficiency of crop | | Revised: August 13,2024 | yield prediction models, which are critical for increasing a nation's production | | Accepted: October 24, 2024 | and food ratio. Complexity also throws off supervised analytical methods, and | | Available Online: October 25, 2024 | nonlinearity grew as the agricultural industry expanded its fields. These fields now encompass a wider range of interconnected elements, including soil type and nutrient content, their relationship to soil water content, air temperature, rainfall, and other factors. In this research, we use quantum computing to solve the problem of handling high-order data more proficiently than the same problems formulated in classical computers. In this paper, we developed and incorporated QSVM and QNN into conventional machine learning models to learn from large and highly complex datasets containing multiple years' worth of | | Keywords:
Quantum Machine Learning
Crop Yield Forecasting
Quantum Computing
Agricultural Technology | | | Soil and Weather Data Analysis | regional and temporal information on soil and weather. We believe these | | | models can reveal patterns that QSVM and QNN are better equipped to detect due to their scalability and ability to compute over large datasets. As a result, the quantum-enhanced models outperform the conventional methods in terms | | Classification Codes: | | | | of predictive power, demonstrating superior MSE values and robustness values. Specifically, the integration of quantum techniques enhanced the generalization ability because of the highly nonlinear relationship between the | | Funding: | | | This research received no specific grant from | variables. These results suggest that QML could significantly improve crop yield | | any funding agency in the public or not-forprofit sector. | estimates, as its predictions are more accurate and directly applicable to agricultural practices and policies. This study would expand the literature on the application of quantum computing in agriculture because it is an emerging field that holds potential for addressing various challenges in food production. In the domain of crop yield prediction, we are laying down the foundations for less vulnerable farming structures that are able to meet the future climate conditions and the growing global food requirements. Thus, the study calls for more research on potential quantum-based solutions in other essential use cases in agriculture. | | | © 2025 The authors published by JCIS. This is an Open Access Article under the Creative Common Attribution Non-Commercial 4.0 | | Corresponding Author’[s Email](s Email: jbasit.msse23mcs@student.nust.edu.pk)[:](s Email: jbasit.msse23mcs@student.nust.edu.pk)[ ](s Email: jbasit.msse23mcs@student.nust.edu.pk)[jbasit.msse23mcs@student.nust.edu.pk](s Email: jbasit.msse23mcs@student.nust.edu.pk), Citation: | | 1. Introduction The world's agriculture finds itself at a crossroads today, majorly propelled by the challenges of feeding the evergrowing population and the intensifying effects of global warming. The United Nations estimates that the global population could double to nearly 10 billion by 2050 [1], necessitating an increase in agricultural food production to sustain this population. Similarly, it also directly affects agricultural production through more frequent and severe subsequent weather conditions, an affected germination calendar, and an increase in the

FAQs

What problem does the paper address in crop yield forecasting?

It addresses limited accuracy of conventional forecasting methods when soil and weather data create highly complex, nonlinear relationships and high-dimensional feature spaces.

Which quantum machine learning methods are introduced?

The study develops and incorporates QSVM and QNN into conventional machine learning models for learning from large, multi-year datasets.

How do the quantum-enhanced models perform compared with conventional methods?

The results report higher predictive power, with superior MSE values and improved robustness, attributed to better generalization under nonlinear relationships.

Aug 5, 2026

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