Research Article
Open Access
A Big Data-Driven Hybrid Ensemble Learning Framework for Crop Yield Prediction in Low-Productivity Districts
1
Department of Computer Science, Banasthali Vidyapith, Rajasthan 304022, India
* Corresponding Author:
Somya Singh,
[email protected]
Volume 2, Issue 3
2026
Received: 30 March 2026
Revised: 04 April 2026
Accepted: 15 September 2026
Published: 22 September 2026
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CC BY 4.0 License
Article Information
Abstract
Low-productivity agricultural belts in India face the compounding realities of land fragmentation, erratic weather, and heterogeneous soils, which together limit harvest outcomes. Precision agriculture and big data analytics together offer an innovative solution that turns large volumes of heterogeneous data into actionable agronomic intelligence. This study proposes a big-data-driven crop yield prediction (CYP) framework tailored to low-productivity districts in India. The architecture combines soil and climatic analytics with data warehousing, machine learning (four base regressors and voting-based hybrid ensembles), and a preprocessing pipeline designed for compatibility with distributed computing frameworks such as Apache Spark, together with SHAP-based explainability for interpreting the model's predictions. The framework provides accurate, district-level yield forecasting to support context-sensitive agronomic decision-making. The paper reports the predictive performance achieved, discusses implementation challenges, and outlines a future research agenda for data-driven agricultural reform in India.
Graphical Abstract
Keywords
crop yield prediction (CYP)
machine learning (ML)
ensemble learning
big data
precision agriculture
explainable AI (SHAP)
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
AI Use Statement
The authors declare that ChatGPT was used to assist with language editing, structural refinement, and selected drafting of the manuscript. The authors have carefully reviewed, revised, and verified the AI-assisted output and take full responsibility for the content of the manuscript.
Ethical Approval and Consent to Participate
Not applicable. This study is based entirely on secondary, district-level agricultural, soil, and climatic datasets that are publicly available government records accessed in accordance with applicable data use policies, and did not involve human participants, personal data collection, or animal experiments.
References
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APA Style
Singh, S., & Gupta, Y. K. (2026). A Big Data-Driven Hybrid Ensemble Learning Framework for Crop Yield Prediction in Low-Productivity Districts. Digital Intelligence in Agriculture, 2(3), 158-167. https://doi.org/10.62762/DIA.2026.295683
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TY - JOUR AU - Singh, Somya AU - Gupta, Yogesh Kumar PY - 2026 DA - 2026/09/22 TI - A Big Data-Driven Hybrid Ensemble Learning Framework for Crop Yield Prediction in Low-Productivity Districts JO - Digital Intelligence in Agriculture T2 - Digital Intelligence in Agriculture JF - Digital Intelligence in Agriculture VL - 2 IS - 3 SP - 158 EP - 167 DO - 10.62762/DIA.2026.295683 UR - https://www.icck.org/article/abs/DIA.2026.295683 KW - crop yield prediction (CYP) KW - machine learning (ML) KW - ensemble learning KW - big data KW - precision agriculture KW - explainable AI (SHAP) AB - Low-productivity agricultural belts in India face the compounding realities of land fragmentation, erratic weather, and heterogeneous soils, which together limit harvest outcomes. Precision agriculture and big data analytics together offer an innovative solution that turns large volumes of heterogeneous data into actionable agronomic intelligence. This study proposes a big-data-driven crop yield prediction (CYP) framework tailored to low-productivity districts in India. The architecture combines soil and climatic analytics with data warehousing, machine learning (four base regressors and voting-based hybrid ensembles), and a preprocessing pipeline designed for compatibility with distributed computing frameworks such as Apache Spark, together with SHAP-based explainability for interpreting the model's predictions. The framework provides accurate, district-level yield forecasting to support context-sensitive agronomic decision-making. The paper reports the predictive performance achieved, discusses implementation challenges, and outlines a future research agenda for data-driven agricultural reform in India. SN - 3069-3187 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Singh2026A,
author = {Somya Singh and Yogesh Kumar Gupta},
title = {A Big Data-Driven Hybrid Ensemble Learning Framework for Crop Yield Prediction in Low-Productivity Districts},
journal = {Digital Intelligence in Agriculture},
year = {2026},
volume = {2},
number = {3},
pages = {158-167},
doi = {10.62762/DIA.2026.295683},
url = {https://www.icck.org/article/abs/DIA.2026.295683},
abstract = {Low-productivity agricultural belts in India face the compounding realities of land fragmentation, erratic weather, and heterogeneous soils, which together limit harvest outcomes. Precision agriculture and big data analytics together offer an innovative solution that turns large volumes of heterogeneous data into actionable agronomic intelligence. This study proposes a big-data-driven crop yield prediction (CYP) framework tailored to low-productivity districts in India. The architecture combines soil and climatic analytics with data warehousing, machine learning (four base regressors and voting-based hybrid ensembles), and a preprocessing pipeline designed for compatibility with distributed computing frameworks such as Apache Spark, together with SHAP-based explainability for interpreting the model's predictions. The framework provides accurate, district-level yield forecasting to support context-sensitive agronomic decision-making. The paper reports the predictive performance achieved, discusses implementation challenges, and outlines a future research agenda for data-driven agricultural reform in India.},
keywords = {crop yield prediction (CYP), machine learning (ML), ensemble learning, big data, precision agriculture, explainable AI (SHAP)},
issn = {3069-3187},
publisher = {Institute of Central Computation and Knowledge}
}
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