Citation

Nair, Rekha R. and Babu, Tina and Yogarayan, Sumendra and Razak, Siti Fatimah Abdul (2026) A Counterfactual-Enabled Agricultural Decision Support Framework for Sustainability-Aware Groundnut Yield Prediction Using Bayesian-Optimized XGBoost. AI, 7 (9). p. 364. ISSN 2673-2688

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Abstract

Sustainable agricultural planning requires predictive frameworks that can capture spatiotemporal variability, sustainability dynamics, and the potential outcomes of alternative management scenarios. The research proposes TSAFI-DT, a retrospectively validated, data-driven Digital Twin prototype integrating spatiotemporal data reconstruction, sustainability-state representation, hierarchical yield forecasting, counterfactual analysis, and scenario simulation. The framework operates on historical district-level APY observations and therefore represents a retrospective approximation of Digital Twin operation rather than a continuously synchronized cyber-physical agricultural Digital Twin. The Extended Regenerative Agriculture Index (eRAI) combines crop diversity, productivity–stability, land-use efficiency, and yield-trend information to characterize district-level sustainability states. A Bayesian-optimized XGBoost model is employed for one-step-ahead yield forecasting under temporal validation, while fixed-effects and synthetic-control analyses provide complementary associational and intervention-associated evidence. Evaluation using district-level groundnut data from India during 1997–2023 demonstrates that the proposed predictor achieves an RMSE of 0.171 t/ha and

Item Type:	Article
Uncontrolled Keywords:	digital twin system, explainable artificial intelligence (XAI), spatiotemporal data analytics, agricultural crop-yield prediction, counterfactual analysis
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	28 Oct 2026 03:45
Last Modified:	28 Oct 2026 03:45
URII:	http://shdl.mmu.edu.my/id/eprint/16927

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