Integrating Harmony and Accountability: A Dual-Engine Ethical Governance Framework and Verifiable Trust Mechanism for Precision Agriculture with Artificial Intelligence

Precision agriculture is gradually becoming a policy tool for countries to promote food security and sustainable governance, with artificial intelligence (AI) playing a key role: through sensors, drones, satellite imagery, and machine learning models, agricultural decisions can be made more precisely to suit soil and climate differences, thereby reducing fertilizer, irrigation, and pesticide inputs. However, embedding AI into agriculture does not necessarily lead to sustainability. On the contrary, the digital infrastructure of precision agriculture may bring about hardware lifecycle carbon footprints and electronic waste; data platforms and algorithms may lead to power concentration, data exploitation, and unfairness to smallholder farmers and diversified farming systems; and may even reinforce monoculture, land concentration, and ecological vulnerability under the guise of an "efficiency narrative." This article argues that existing AI ethics mostly focus on abstract principles such as transparency, fairness, and privacy, lacking governance tools that can be directly applied to agricultural ecosystems and rural justice; while traditional environmental ethics provides value critique, it often lacks institutionalized operational and accountability mechanisms. To this end, this paper proposes a "dual-engine governance model": using the relational ontology of Eastern ecological ethics and the spirit of restraint embodying the "unity of man and nature" as the value engine to define the purpose, boundaries, and inviolable ecological bottom line of AI intervention in agriculture; and using responsibility, rights, and procedural justice from Western ethics as the procedural engine to transform value requirements into an auditable, appealable, and remedial system. This paper further integrates the dual engines with the concept of "verifiable trust," proposing a set of "AI Environmental Impact Assessment" (AIEIA) methods and workflows for precision agriculture, including indicator design, data governance, model lifecycle auditing, stakeholder participation, and remedial mechanisms. Finally, this paper uses scenario-based case studies to demonstrate how AIEIA can reveal backlash effects and data injustice, and proposes policy and industry implementation recommendations to promote agricultural AI towards ecological prosperity rather than merely efficiency optimization.