TriAlignXA: An Explainable Trilemma Alignment Framework for Trustworthy Agri-product Grading

Quality is foundational to trust in online produce sales: The research confirms through a "Trust Pyramid" model that product quality serves as the cornerstone of consumer trust in digital fruit and vegetable transactions, addressing the "trust deficit" created when buyers cannot directly inspect items before purchase.

Agricultural grading involves unavoidable trade-offs: The paper identifies an "impossible triangle" showing that traditional grading standards cannot simultaneously optimize biological quality characteristics, timeliness of delivery, and economic viability—revealing fundamental constraints that any grading system must navigate rather than ignore.

AI should inform rather than decide: TriAlignXA redefines algorithms' role from making autonomous grading decisions to providing transparent, explainable information that supports human decision-making, shifting toward a more trustworthy approach to AI deployment in commerce.

Transparency mechanisms can build confidence: The framework uses a "Pre-Mapping Mechanism" that encodes process and quality data into QR codes, allowing consumers to access detailed information about how products were graded and handled—making the previously opaque grading process visible and verifiable.

Multi-objective optimization outperforms traditional approaches: Experiments demonstrate that the framework achieves "significantly higher accuracy" than baseline models by simultaneously balancing quality assessment, processing efficiency, and cost control rather than optimizing for a single metric.