The study evaluates machine learning for nitrogen recommendations in winter wheat by directly scoring profit loss on 892 yield response curves, rather than relying on prediction accuracy. Results show that ML models alone fail to recover the best nitrogen rate within farm tolerance and underperform standard UK advice across price scenarios. However, a simple post‑model correction step significantly reduces profit losses, and a hybrid approach combining standard advice with a damped correction eliminates bias and large losses.
By Xulong Wang, Po Yang
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
The study evaluates machine learning models for nitrogen recommendations in winter wheat by directly scoring profit loss on 892 yield response curves, rather than relying on prediction accuracy. Results show that none of the models recover the best rate within farm tolerance, and at typical prices all models underperform standard UK advice. A simple post‑model correction step reduces profit losses by up to 43% without retraining, while a hybrid approach further mitigates bias and large losses.
By Xulong Wang, Po Yang
The paper investigates how a single pooled contract offered by an aggregator to heterogeneous smallholder farmers can be designed to maximize profit while addressing private adoption costs and unobserved effort over multiple seasons. Using a POMDP framework and reinforcement learning, the authors find that profit‑maximizing contracts disproportionately favor large farms, achieving 87.7% of possible adoption on large farms versus only 8.2% on smallholdings, largely due to higher measurement, reporting, and verification costs on smaller plots. The study suggests that adjusting MRV cost structures could reduce this disparity and help scale carbon farming to smallholders.
By Rishi Bharadwaj, Yadati Narahari
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.
By Vedant Balasubramaniam, Geetha Charan, Manojkumar Patil, Rohit P Suresh, V Priyanka, Kodur Sai Vinay Sathvik, Y. Narahari
The paper evaluates machine learning for nitrogen recommendations in winter wheat by directly scoring the profit lost on measured yield response curves, rather than relying on prediction accuracy. Using 892 yield curves from UK experiments, the authors find that machine learning models fail to recover the best nitrogen rate within farm tolerance and generally underperform standard UK advice in terms of profit. However, a simple post‑model correction step can reduce profit losses by up to 43% without retraining, suggesting that machine learning can enhance standard advice rather than replace it.
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
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. 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:2607. 10694v1 Announce Type: cross Abstract: We study the problem of optimal continual fine-tuning for a pre-trained Foundation Model deployed at a resource-limited device.
By Thomas Tsouparopoulos, Iordanis Koutsopoulos
The paper explores how data from fixed A/B tests can guide the deployment of adaptive experiments using contextual bandits. By combining off‑policy evaluation with a controlled warm‑start simulation, the authors rank pre‑specified adaptive and non‑adaptive policies using doubly robust estimators. Experiments on synthetic trials and real benchmarks show that adaptive, context‑aware policies outperform fixed allocations when heterogeneity exists, but offer little advantage otherwise.
By Jo\~ao Victor Ferreira Alves, Eduardo Rocha Laurentino, Gustavo de Oliveira Kanno, Thiago Costa Rizuti da Rocha
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