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
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.
By Ao Qu, Panagiotis Michelakis, Linyuan Han, Yiannis Hadjiyianni, Kun Ouyang, Konstantinos Siskos, Feng Li, Ran Meng, Jingchi Jiang, Dimitrios Stamoulis, Jie Liu
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.
By Sakshi Arya, Wentao Lin
arXiv:2608. 07801v1 Announce Type: cross Abstract: Precision nitrogen (N) management (PNM) for cotton requires in-season monitoring of crop growth parameters and N status indicators to decide fertilizer timing, placement, and application rates for optimal canopy development and yield.
By Vaishali Swaminathan, Nithya Rajan, J Alex Thomasson, Amrit Shrestha, Karem Meza Capcha, Robert Hardin, Pramod Pokhrel
arXiv:2608. 09949v1 Announce Type: new Abstract: This study evaluates the application of Large Language Models (LLMs) in complex biological systems, evolving from data analysis to autonomous, AI-guided experimentation.
By Serge Kernbach
arXiv:2607. 23880v1 Announce Type: cross Abstract: Nitrous oxide (N$_2$O) is the dominant ozone-depleting substance emitted in the 21st century, and the third largest contributor to anthropogenic greenhouse gases due to its high potency and long atmospheric lifetime, with more than 70% of N$_2$O emissions occurring as a result of agricultural processes.
By Freddy Yu, Jashanjeet Kaur Dhaliwal, Subhadeep Chakraborty
arXiv:2504. 18451v2 Announce Type: replace Abstract: Rapid global population growth underscores the need for digitally enabled agricultural systems that support sustainable food production and data-driven resource management for farmers and stakeholders.
By Tewodros Alemu Ayall, Andy Li, Matthew Beddows, Milan Markovic, Georgios Leontidis
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.
By Tomoaki Yamaguchi, Yutong Zhou, Masahiro Ryo, Keisuke Katsura
arXiv:2608.02270v2 Announce Type: replace-cross
Abstract: Agriculture 4.0 robotic systems improve field efficiency yet remain too capital-intensive for the fragmented smallholdings that dominate glob...
By Weijie Shi, Zicheng Xu, Zhenbang Cheng, Haoran Xuan, Mingbo Duan, Gan Ge
arXiv:2506. 03168v2 Announce Type: replace-cross Abstract: Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital transformation to facilitate efficient big data processing.
By Dawen Jiang, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Wei Xiang, Jiong Jin
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:2609.12505v1 Announce Type: new
Abstract: Vision-based reinforcement learning holds strong potential for decision-making in controlled environment agriculture (CEA). However, its development is...
By Ziye Zhu, Bert van 't Ooster, Congcong Sun, Eldert van Henten, Sjoerd Boersma