Monte Carlo-Based Ex-Ante Assessment of the Green Benefits of an AI-Driven Smart Agriculture Platform in Hainan

• URL: http://arxiv.org/abs/2609.06737v1
• Date: Sun, 06 Sep 2026 17:19:43 GMT
• Title: Monte Carlo-Based Ex-Ante Assessment of the Green Benefits of an AI-Driven Smart Agriculture Platform in Hainan
• Abstract summary: This study builds a cradle-to-gate agricultural carbon accounting model covering pesticide and fertilizer production, field N2O, irrigation electricity and paddy CH4.<n>It translates platform interventions into quantifiable transmission parameters, and propagates parameter uncertainty by Monte Carlo simulation.<n>It offers a calibration-ready methodology for ex-ante green-value assessment and pilot observation design.
• Score: 12.93848838655257
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Smart agriculture platforms are widely regarded as key carriers for implementing China's pesticide and fertilizer reduction, water-saving and carbon-reduction agendas, yet a unified quantitative framework for assessing their green value is still lacking. Taking an AI-driven decision platform for tropical agriculture as the object (integrating large-language-model question answering, multimodal pest diagnosis, IoT sensing, satellite remote sensing, and a closed-loop field record system), this study builds a cradle-to-farm-gate agricultural carbon accounting model covering pesticide and fertilizer production, field N2O, irrigation electricity and paddy CH4, translates platform interventions into quantifiable transmission parameters, and propagates parameter uncertainty by Monte Carlo simulation over three Hainan scenarios (mango, winter vegetable, rice/nanfan, area-weighted 40%:30%:30%). Under full adoption, median reductions are 23.5% (90% interval 15.0%-33.2%) for pesticide use, 21.0% (13.8%-28.9%) for fertilizer, 16.5% (10.9%-23.5%) for irrigation water, and 21.5% (16.1%-27.2%) for carbon intensity. Attainment probabilities are high for fertilizer reduction >=15% (90.6%) and clear carbon decline (98.1%), but only about 20% for aggregate water saving >=20%, favoring scenario-specific statements. Sobol first-order indices show soil-test recommendation and organic substitution jointly explain about 83% of the variance of aggregate carbon-intensity reduction. Convergence tests show 10,000 iterations stabilize all statistics; conservative/baseline/optimistic scenario bounds are reported. The framework offers a reproducible, calibration-ready methodology for ex-ante green-value assessment and pilot observation design.

Related papers

• Simulating the Marginal Green Contribution of AI Modules in a Smart-Agriculture Platform: Evidence from Two Monte Carlo Experiments [12.93848838655257]
  This paper makes the components explicit and runs two controlled simulation experiments.<n>Experiment 1 follows the chain from AI capability to farmer behavior to agrochemical input reduction.<n>Experiment 2 compares current practice (P0), IoT engineering retrofit (P1), and P1 plus AI irrigation scheduling.
  arXiv  Detail & Related papers  (2026-09-06T17:25:33Z)
• TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation [24.675294562363252]
  Time series generation is a natural remedy, but existing GAN and diffusion-based generators often provide limited explicit supervision for the domain structure of carbon emission data.<n>We propose TriHead-GAN, a Transformer-based adversarial framework whose triple-head discriminator jointly supervises three complementary aspects of the joint distribution.<n>The generator combines global self-attention with local temporal convolution, per-step noise injection, and an anti-smoothing loss that matches first-difference statistics.
  arXiv  Detail & Related papers  (2026-05-26T02:48:56Z)
• GreenSeg: Ground Segmentation Algorithm for Agricultural Robots in Mediterranean Greenhouses using RGB-D Point Clouds [41.99844472131922]
  GreenSeg is a robust perception framework for autonomous navigation using RGB-D sensing.<n>Results show that GreenSeg consistently outperforms benchmark segmentation methods.
  arXiv  Detail & Related papers  (2026-05-24T22:19:38Z)
• Progressive Generalization Augmentation with Deeply Coupled RND-PPO and Domain-Prioritized Noise Injection for Robust Crop Management Reinforcement Learning [3.0094301461257476]
  This paper tackles three limitations impeding practical deployment of agricultural RL systems.<n>We introduce three systematic innovations: Progressive Generalization Augmentation (PGA) implementing a three-phase curriculum; a deeply coupled RND-PPO architecture with dual-channel GAE normalization, progress-decayed intrinsic coefficients, and semantic discretization; and domain-prioritized noise injection with hierarchical activation.
  arXiv  Detail & Related papers  (2026-05-17T12:48:47Z)
• NDRL: Cotton Irrigation and Nitrogen Application with Nested Dual-Agent Reinforcement Learning [30.1462125315719]
  We propose a Nested Dual-Agent Reinforcement Learning (NDRL) method to improve yield optimization.<n>The child agent's reward function incorporates quantified Water Stress Factor (WSF) and Nitrogen Stress Factor (NSF)<n> Experimental results demonstrate that, compared to the best baseline, the simulated yield increased by 4.7% in both 2023 and 2024.
  arXiv  Detail & Related papers  (2025-12-18T11:07:35Z)
• AI-Driven Carbon Monitoring: Transformer-Based Reconstruction of Atmospheric CO2 in Canadian Poultry Regions [0.0]
  We present a framework that reconstructs continuous, uncertainty-quantified XCO2 fields from OCO-2 across southern Canada.<n>The model fuses wavelet time-frequency representations with transformer attention over meteorology, vegetation indices, topography, and land cover.
  arXiv  Detail & Related papers  (2025-10-26T10:41:12Z)
• AgroSense: An Integrated Deep Learning System for Crop Recommendation via Soil Image Analysis and Nutrient Profiling [0.9974630621313314]
  AgroSense is a deep-learning framework that integrates soil image classification and nutrient profiling to produce accurate and contextually relevant crop recommendations.<n>A multimodal dataset of 10,000 paired samples drawn from publicly available Kaggle repositories was curated.<n>A fused model achieves 98.0% accuracy, with a precision of 97.8%, a recall of 97.7%, and an F1-score of 96.75%.
  arXiv  Detail & Related papers  (2025-09-01T10:25:05Z)
• The unrealized potential of agroforestry for an emissions-intensive agricultural commodity [48.10353372557444]
  Increasing shade-tree cover to a minimum of 30% could sequester an additional 307 million tonnes of CO2e.<n>This could offset 167% of contemporary cocoa-related emissions in Ghana and Cote d'Ivoire.
  arXiv  Detail & Related papers  (2024-10-28T10:02:32Z)
• Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height and Cover from High-Resolution, Multi-Sensor Satellite Imagery [0.196629787330046]
  We present a new methodology which uses multi-sensor, multi-spectral imagery of 10 meters and a deep learning based model which unifies the prediction of above ground biomass density (AGBD), canopy height (CH), canopy cover (CC)<n>The model is trained on millions of globally sampled GEDI-L2/L4 measurements. We validate the capability of our model by deploying it over the entire globe for the year 2023 as well as annually from 2016 to 2023 over selected areas.
  arXiv  Detail & Related papers  (2024-08-20T23:15:41Z)
• Machine Learning in management of precautionary closures caused by lipophilic biotoxins [43.51581973358462]
  Mussel farming is one of the most important aquaculture industries. The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption. This work proposes a predictive model capable of supporting the application of precautionary closures.
  arXiv  Detail & Related papers  (2024-02-14T15:51:58Z)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• A multiscale spatiotemporal approach for smallholder irrigation detection [0.0]
  This paper presents an irrigation detection methodology that leverages multiscale satellite imagery of vegetation abundance. The methodology is applied to detect smallholder irrigation in two states in the Ethiopian highlands, Tigray and Amhara.
  arXiv  Detail & Related papers  (2022-02-09T02:50:42Z)
• High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR [27.25600860698314]
  Deforestation, which causes high carbon emissions and threatens biodiversity, is often linked to agricultural expansion. We propose an automated approach that aims to support conservation and sustainable land use planning decisions. A deep learning approach is developed that estimates canopy height for each 10 m Sentinel-2 pixel by learning from sparse GEDI LIDAR reference data. We show that these wall-to-wall maps of canopy top height are predictive for classifying HCS forests and degraded areas with an overall accuracy of 86 % and produce a first high carbon stock map for Indonesia, Malaysia, and the Philippines.
  arXiv  Detail & Related papers  (2021-07-15T16:21:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.