Smart‑agriculture platforms usually bundle AI diagnosis, IoT sensing and decision push, making it hard to attribute green benefits to each component and leaving resource allocation without quantitative evidence. Building on a previous platform‑level Monte Carlo assessment, this paper makes the components explicit and conducts two controlled simulation experiments.\ \ Experiment 1 follows the chain AI capability → farmer behavior → pesticide/fertilizer input reduction. The pesticide‑reduction model is\ $$R_{p}=s_{blind}\times e_{presc}\times c_{touch}\times a$$\ where $s_{blind}$ is the avoidable blind‑application share, $e_{presc}$ the prescription effectiveness, $c_{touch}$ the decision‑touch coverage, and $a$ the adoption rate. Fertilizer reduction uses the same structure. Compared with an experienced‑extension mode, the probability of achieving a 20% pesticide reduction is essentially zero in the extension mode, 20.7% at baseline for AI, and rises to 49% when diagnosis accuracy is 0.95 and adoption is 0.85. The probability of a 15% fertilizer reduction climbs from near zero to 52.0%.\ \ Experiment 2 compares current practice (P0), IoT engineering retrofit (P1), and P1 plus AI irrigation scheduling (P2). Median aggregate water saving increases from 7.8% (P0) to 11.0% (P1) and 16.0% (P2), with AI adding 5.0 percentage points beyond engineering alone. Paddy CH4 emissions drop to 30.5% under AI scheduling versus 19.8% with manual operation, and the rice‑irrigation‑methane subsystem’s carbon intensity declines by 27.9%.\ \ Sensitivity analyses consistently indicate that farmer adoption, not algorithm accuracy, is the primary bottleneck for meeting green targets, and that AI data fusion remains robust to soil‑moisture sensing errors.\ \ This work provides a reproducible simulation framework for component‑level green‑value evaluation and promotion‑strategy optimization of smart‑agriculture platforms.\ \ Review: The experiments highlight farmer adoption as the decisive factor for green impact; while high AI accuracy contributes, effective outreach and incentive mechanisms are essential to unlock the full environmental benefits of smart‑agriculture systems.