AgriGPT: a Large Language Model Ecosystem for Agriculture

Despite the rapid progress of Large Language Models (LLMs), their application in agriculture remains limited due to the lack of domain-specific models, curated datasets, and robust evaluation frameworks. To address these challenges, we propose AgriGPT, a domain-specialized LLM ecosystem for agricultural usage. At its core, we design a multi-agent scalable data engine that systematically compiles credible data sources into Agri-342K, a high-quality, standardized question-answer (QA) dataset. Trained on this dataset, AgriGPT supports a broad range of agricultural stakeholders, from practitioners to policy-makers. To enhance factual grounding, we employ Tri-RAG, a three-channel Retrieval-Augmented Generation framework combining dense retrieval, sparse retrieval, and multi-hop knowledge graph reasoning, thereby improving the LLM's reasoning reliability. For comprehensive evaluation, we introduce AgriBench-13K, a benchmark suite comprising 13 tasks with varying types and complexities. Experiments demonstrate that AgriGPT significantly outperforms general-purpose LLMs on both domain adaptation and reasoning. Beyond the model itself, AgriGPT represents a modular and extensible LLM ecosystem for agriculture, comprising structured data construction, retrieval-enhanced generation, and domain-specific evaluation. This work provides a generalizable framework for developing scientific and industry-specialized LLMs. All models, datasets, and code will be released to empower agricultural communities, especially in underserved regions, and to promote open, impactful research.

Skeleton Detection Using Dual Radars with Integration of Dual-View CNN Models and mmPose

Skeleton detection is a technique that can beapplied to a variety of situations. It is especially critical identifying and tracking the movements of the elderly, especially in real-time fall detection. While conventional image processing methods exist, there's a growing preference for utilizing pointclouds data collected by mmWave radars from viewpoint of privacy protection, offering a non-intrusive approach to elevatesafety and care for the elderly. Dealing with point cloud data necessitates addressing three critical considerations. Firstly, the inherent nature of point clouds -- rotation invariance, translation invariance, and locality -- is managed through the fusion of PointNet and mmPose. PointNet ensures rotational and translational invariance, while mmPose addresses locality. Secondly, the limited points per frame from radar require data integration from two radars to enhance skeletal detection. Lastly,inputting point cloud data into the learning model involves utilizing features like coordinates, velocity, and signal-to-noise ratio (SNR) per radar point to mitigate sparsity issues and reduce computational load. This research proposes three Dual ViewCNN models, combining PointNet and mmPose, employing two mmWave radars, with performance comparisons in terms of Mean Absolute Error (MAE). While the proposed model shows suboptimal results for random walking, it excels in the arm swing case.