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

The 'trust deficit' in online fruit and vegetable e-commerce stems from the inability of digital transactions to provide direct sensory perception of product quality. This paper constructs a 'Trust Pyramid' model through 'dual-source verification' of consumer trust. Experiments confirm that quality is the cornerstone of trust. The study reveals an 'impossible triangle' in agricultural product grading, comprising biological characteristics, timeliness, and economic viability, highlighting the limitations of traditional absolute grading standards. To quantitatively assess this trade-off, we propose the 'Triangular Trust Index' (TTI). We redefine the role of algorithms from 'decision-makers' to 'providers of transparent decision-making bases', designing the explainable AI framework--TriAlignXA. This framework supports trustworthy online transactions within agricultural constraints through multi-objective optimization. Its core relies on three engines: the Bio-Adaptive Engine for granular quality description; the Timeliness Optimization Engine for processing efficiency; and the Economic Optimization Engine for cost control. Additionally, the "Pre-Mapping Mechanism" encodes process data into QR codes, transparently conveying quality information. Experiments on grading tasks demonstrate significantly higher accuracy than baseline models. Empirical evidence and theoretical analysis verify the framework's balancing capability in addressing the "impossible triangle". This research provides comprehensive support--from theory to practice--for building a trustworthy online produce ecosystem, establishing a critical pathway from algorithmic decision-making to consumer trust.