arXiv AI
How Reliable Are Large Language Models for Soil Organic Carbon Assessment? An Australian Benchmark
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Sep 10
Monte Carlo-Based Ex-Ante Assessment of the Green Benefits of an AI-Driven Smart Agriculture Platform in Hainan
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 Machine Learning
Jul 2
Spectroscopy Analysis with Machine Learning Regression for the Quantification of Carbon and Nitrogen Contents in Inceptisol and Oxisol Soil Types: Comparing Different Preprocessing and Validation methods as well as Feature Importance
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.
arXiv AI
Oct 19
Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model
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 AI
Jun 30
Fine-Tuning General-Purpose Large Language Models for Agricultural Applications:A Reproducible Framework and Evaluation Protocol Based on Qwen3-8B
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 Machine Learning
Jun 16
Multi-Modal Spatio-Temporal Graph Neural Network with Mixture of Experts for Soil Organic Carbon Prediction
arXiv:2606. 16580v1 Announce Type: new Abstract: Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning.
arXiv Machine Learning
Aug 13
Transferable Above-Ground Biomass (AGB) Estimation Model from Multi-Sensor Data with Sparse Field Calibration
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.