arXiv:2607. 07759v1 Announce Type: new Abstract: Agricultural supply chains are vulnerable to disruptions through linked biophysical and economic systems.
arXiv:2608. 09846v1 Announce Type: new Abstract: This paper presents a methodological framework for real-time climate risk assessment using data-driven nowcasting techniques to enhance supply chain resilience in Colombian agricultural contexts.
arXiv:2506. 04571v3 Announce Type: replace Abstract: Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies.
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: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.
arXiv:2605. 03648v2 Announce Type: replace Abstract: To understand complex system dynamics in dairy farming requires tools that capture farm heterogeneity, social interactions, and cumulative environmental impacts.
Google DeepMind and Isomorphic Labs are sharing our joint approach to bioresilience and AI models.
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.
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.
arXiv:2507. 17012v2 Announce Type: replace Abstract: Reducing the rapidly growing environmental impact of the computing industry requires assessing the emissions of electronics at scale.
A new algorithm has been developed that can generate scenarios for extreme events, even when there is no historical data on such events. The algorithm is designed to anticipate unprecedented situations that critical infrastructure and global supply chains are least prepared for. By creating these scenarios, the tool aims to help stakeholders better understand and plan for potential disruptions.
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...
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.
