Risk-Aware Multi-Objective Decision Intelligence for Precision Agriculture Using Human-in-the-Loop Optimization and Visual Analytics

Paper ID: EIJTEM_2025_12_4_316-326

Author's Name: R. Indrakumari, Dr. Vivek Kumar

Volume: 12

Issue: 4

Year: 2025

Page No: 316-326

Abstract:

Precision agriculture increasingly employs predictive analytics to forecast crop yield and resource demand; however, decision-making under uncertainty remains a persistent challenge. Most existing agricultural decision support systems rely on single-objective optimization or deterministic rules, limiting their ability to balance competing objectives and manage climatic risk. This paper presents a risk-aware multi-objective decision intelligence framework that explicitly integrates predictive uncertainty, Pareto-based optimization, and human-in-the-loop decision refinement. Agricultural management is formalized as a stochastic multi-objective optimization problem, simultaneously considering yield maximization, water-use efficiency, and decision robustness. Uncertainty arising from weather variability and model estimation is propagated through the optimization layer using probabilistic risk metrics. Visual analytics is employed to enable interactive exploration of Pareto-optimal solutions and risk–reward trade-offs, allowing stakeholders to adapt preferences dynamically. Experimental evaluation using real-world agricultural datasets demonstrates that the proposed framework consistently outperforms deterministic and single-objective baselines, achieving improved yield stability and reduced downside risk under climatic stress. The results highlight the necessity of integrating optimization, uncertainty modeling, and human-centered interaction for robust agricultural decision intelligence.

Keywords: Decision Intelligence; Multi-Objective Optimization; Risk-Aware Decision-Making; Precision Agriculture; Human-in-the-Loop Systems; Visual Analytics

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