模拟智能农业平台中AI模块的边际绿色贡献：来自两项蒙特卡洛实验的证据
Simulating the Marginal Green Contribution of AI Modules in a Smart-Agriculture Platform: Evidence from Two Monte Carlo Experiments
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中文总结 AI 辅助
通过两项蒙特卡洛实验，模拟智能农业平台中AI模块的边际绿色贡献，发现农民采纳是主要瓶颈，AI显著提升农药化肥减少及节水减排效果。
中文摘要 AI 辅助
智能农业平台通常将AI诊断、物联网感知和决策推送捆绑在一个单一包中，因此每个组件可归因的绿色效益仍不明确，资源分配决策缺乏定量证据。基于先前平台级蒙特卡洛评估，本文使组件显式化并运行两项受控模拟实验。实验1遵循从AI能力到农民行为再到农用化学品投入减少的链条，将农药/化肥减少建模为可避免的盲目施用比例乘以处方有效性乘以决策触达覆盖率乘以采纳率，并比较经验推广模式与AI模式：在推广模式下达到20%农药减少的概率基本为零，而在AI模式下基线为20.7%，在诊断准确率0.95和采纳率0.85下高达49%；15%化肥减少的概率从接近零上升到52.0%。实验2比较当前实践（P0）、物联网工程改造（P1）以及P1加AI灌溉调度（P2）：中位数总节水量从7.8%（P0）上升到11.0%（P1）和16.0%（P2），AI在工程基础上额外增加5.0个百分点；稻田CH4减少在AI调度下达30.5%，而人工操作为19.8%，水稻灌溉-甲烷子系统碳强度下降27.9%。两项实验的敏感性分析一致表明，实现绿色目标的主要瓶颈是农民采纳而非算法准确性，且AI数据融合对土壤水分传感误差具有鲁棒性。这项工作为智能农业平台的组件级绿色价值评估和推广策略优化提供了可复现的模拟框架。
英文摘要
Smart agriculture platforms usually bundle AI diagnosis, IoT sensing and decision push into a single package, so the green benefit attributable to each component remains unclear and resource-allocation decisions lack quantitative evidence. Building on a previous platform-level Monte Carlo assessment, this paper makes the components explicit and runs two controlled simulation experiments. Experiment 1 follows the chain from AI capability to farmer behavior to agrochemical input reduction, modeling pesticide/fertilizer reduction as avoidable blind-application share times prescription effectiveness times decision-touch coverage times adoption rate, and compares an experienced-extension mode with the AI mode: the probability of reaching 20% pesticide reduction is essentially zero in the extension mode but 20.7% at baseline, up to 49% with diagnosis accuracy 0.95 and adoption 0.85 under AI; the probability of 15% fertilizer reduction rises 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 rises from 7.8% (P0) to 11.0% (P1) and 16.0% (P2), with AI adding 5.0 percentage points beyond engineering; paddy CH4 reduction reaches 30.5% under AI scheduling versus 19.8% under manual operation, and the rice irrigation-methane subsystem carbon intensity declines 27.9%. Sensitivity analyses of both experiments consistently indicate that the primary bottleneck for meeting green targets is farmer adoption rather than algorithm accuracy, and that AI data fusion is 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.
发表机构
- Sanya University（三亚学院）
- Handan Weizhi Jingjie AI Basic Software Co., Ltd.（邯郸未至之境人工智能基础软件有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。
