The paper presents a Monte Carlo-based framework to quantify the green benefits of an AI-driven smart agriculture platform in Hainan. By integrating large-language-model question answering, multimodal pest diagnosis, IoT sensing, satellite remote sensing, and a closed-loop field record system, the study builds a cradle-to-farm-gate carbon accounting model and simulates three crop scenarios (mango, winter vegetable, rice). Results show median reductions of 23.5% in pesticide use, 21.0% in fertilizer, 16.5% in irrigation water, and 21.5% in carbon intensity, with high probabilities for fertilizer and carbon reductions but lower for water savings.
By Zhaoyang Li, Ruijie Zhang, Zhaoji Sun, Lu Zhang
arXiv:2607. 00834v1 Announce Type: new Abstract: Near-Infrared (NIR) spectroscopy has emerged as a promising alternative to traditional soil analysis methods, offering advantages such as speed, low cost, and non-destructive testing.
By Vinicius Herique Kieling, Guilherme Macedo Baggio, Felipe Augusto Bueno Rossi, Marco Antonio de Castro Barbosa, Dalcimar Casanova, Larissa Macedo dos Santos Tonial, Jefferson Tales Oliva
The paper presents a new method for sensitivity analysis of decision rules in an agent‑based agro‑ecological model. It treats the decision structure as a hierarchical design space, using a random‑forest‑supervised HSIC‑ANOVA to rank main effects and interactions across both categorical and numerical variables. The approach is applied to outputs such as nitrogen leaching, soil organic carbon variation, and yield predictions, providing an interpretable, decision‑oriented analysis.
By Benjamin Pianet, Baptiste Lesquoy, Paul Saves, Benoit Gaudou, Jean Villerd, Nicolas Verstaevel, Olivier Therond
arXiv:2606. 28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text generation.
By Zhaoyang Li, Ruijie Zhang, Jiaqi Liu, Zhaoji Sun
arXiv:2606. 16580v1 Announce Type: new Abstract: Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning.
By Daniele Mos, Felipe Drummond, Anton Bossenbroek, Soufiane el Khinifri
arXiv:2608. 11638v1 Announce Type: new Abstract: Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable.
By Pann Thinzar Seint, Bryan Atwood, Subas Chhatkuli
The paper evaluates the green benefits of individual AI modules within a smart‑agriculture platform through two Monte Carlo simulations. In Experiment 1, AI diagnosis significantly increases the likelihood of reducing pesticide and fertilizer use compared to traditional extension services, with up to a 49 % probability of a 20 % pesticide reduction when accuracy and adoption are high. Experiment 2 shows that adding AI irrigation scheduling to IoT‑based engineering yields a 5 percentage‑point increase in median water savings and a 30.5 % reduction in paddy methane emissions, while farmer adoption remains the key limiting factor for achieving green targets.
By Zhaoyang Li, Ruijie Zhang, Zhaoji Sun, Lu Zhang
AgriJev is a family of adapted Qwen3.5 models (0.8B, 2B, 4B, and 9B) designed for efficient agricultural candidate scoring. The models use a shared interface that conditions on agricultural text, a judgment question, and candidate definitions to return scores and relative probabilities in a single forward pass. Across 17 external test views, AgriJev-9B achieves 75.44% mean accuracy, surpassing both the original Qwen3.5 and matched agricultural SFT models, while also offering roughly half the median scoring latency of equivalent answer‑generation models on an A100 GPU.
By Xuechen Li (University of Minnesota Twin Cities)
arXiv:2606. 12821v1 Announce Type: new Abstract: Environmental scientists spend disproportionate effort on data wrangling rather than analysis, and AI agents that automate geospatial workflows remain unvalidated: no benchmark evaluates agents operating through structured tool calling against real APIs.
By Gabriel Diaz-Ireland, Diego Prieto-Herr\'aez, Mario Garc\'ia Peces, Javier Vel\'azquez, Devika Jain
arXiv:2608. 00608v1 Announce Type: new Abstract: Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties.
By Viacheslav Barkov, Jonas Schmidinger, Robin Gebbers, Martin Atzmueller
arXiv:2610.18378v1 Announce Type: new
Abstract: Applying process-based Earth science models to new sites and questions often requires expert calibration decisions. Reliable delegation to language-mod...
By Zhanliang Zhu, Ziwei Li, Liujun Zhu, Yuchen Liu, Junliang Jin, Jianyun Zhang
The paper highlights that as Large Language Models grow in capability and prevalence, their environmental footprint is increasing, yet the machine learning community lacks standardized carbon accounting practices. An automated review of 5,285 NeurIPS 2025 papers shows almost no reporting of environmental impact. To address this, the authors propose standardized sustainability metrics for training efficiency, heuristics for estimating inference carbon costs, a software tool called carbonbenchmark for tracking emissions, and the SMAJ framework to encourage prioritizing computational efficiency and environmental accountability over marginal accuracy gains.
By Lachlan McGinness, Dan Pagendam, Robert Offner