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.
arXiv:2509. 01924v4 Announce Type: replace-cross Abstract: Agricultural decision-making faces a dual challenge: sustaining high yields to meet global food security needs while reducing the environmental impacts of input use, including fertilizer losses and other agrochemical applications such as herbicides, insecticides, and fungicides.
arXiv:2607. 00454v1 Announce Type: new Abstract: Agricultural advisory systems face a fundamental tension: static agronomic guidelines offer consistent, evidence-based recommendations, yet remain blind to in-season variability and dynamic uncertainties.
arXiv:2608.30392v1 Announce Type: new Abstract: Global food security and sustainable climate action increasingly rely on robust, scalable agricultural monitoring. Earth observation foundation models...
PiCo is a post‑hoc framework that explains Geo‑foundation model predictions by identifying concept vectors in the model’s embedding space. It generates synthetic multispectral satellite data using a 3D radiative transfer model and a game engine to reveal physical sensitivities, then projects embeddings onto these vectors to quantify concept importance. Evaluations on three GFMs show that PiCo can recover most of the full‑embedding accuracy and uncover how different physical concepts influence predictions for tasks such as ground‑cover estimation and forest classification.
Geo-foundation models (GFMs) are typically evaluated by downstream task predictive performance, revealing little about which physical features they are sensitive to or use in prediction. This limits d...
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.
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.
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.
The paper introduces FAIRY, a full-stack smart‑agriculture agent system deployed on a soybean research farm at Harbin Institute of Technology. FAIRY executes and evaluates end‑to‑end agronomic operations—from ridge preparation to storage—using an event‑driven world model that integrates machinery, sensors, drones, satellite data, weather, crop models, and historical yields. The system implements a comprehensive agentic stack and is used to benchmark nine state‑of‑the‑art agent controllers across 100 full‑season soybean scenarios, assessing success, spatiotemporal correctness, token cost, and edge‑device runtime.
Mimir is a physics‑grounded large language model agent designed for long‑horizon irrigation control. It operates on two timescales: a fast scale that uses a structured physical interface and deterministic simulator to validate and refine LLM proposals before execution, and a slow scale that consolidates recurrent failure patterns into persistent contextual principles. Across multiple sites, crops, and years, Mimir achieves the lowest aggregate control cost and reduces irrigation usage by about 51% compared to historical schedules, while ablation studies confirm the importance of forward simulation, verified revision, and persistent context.
arXiv:2512. 21066v4 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) reveals how explanatory variables relate to a response variable, yet communicating XAI outputs to laypersons remains difficult, limiting trust in AI-based predictions.
arXiv:2610.18201v1 Announce Type: cross Abstract: Soil organic carbon (SOC) is an indicator of agricultural soil condition and carbon storage, but numerical outputs from large language models (LLMs)...