OpenAg: Democratizing Agricultural Intelligence

TL;DR

OpenAg is a comprehensive framework that combines domain-specific models, knowledge graphs, multi-agent reasoning, and explainability to provide context-aware, actionable agricultural insights for smallholder farmers.

Contribution

It introduces a novel integrated system that enhances agricultural AI with domain knowledge, reasoning, and transparency, addressing limitations of general-purpose large language models.

Findings

Developed a unified agricultural knowledge base integrating diverse data sources.

Implemented a neural knowledge graph for structured reasoning in agriculture.

Created an adaptive multi-agent reasoning system for domain-specific collaboration.

Abstract

Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies. However, current agricultural intelligence systems often lack contextual understanding, explainability, and adaptability, especially for smallholder farmers with limited resources. General-purpose large language models (LLMs), while powerful, typically lack the domain-specific knowledge and contextual reasoning needed for practical decision support in farming. They tend to produce recommendations that are too generic or unrealistic for real-world applications. To address these challenges, we present OpenAg, a comprehensive framework designed to advance agricultural artificial general intelligence (AGI). OpenAg combines domain-specific foundation models, neural knowledge graphs, multi-agent reasoning, causal explainability, and adaptive transfer learning to deliver context-aware, explainable, and actionable insights. The system includes: (i) a unified agricultural knowledge base that integrates scientific literature, sensor data, and farmer-generated knowledge; (ii) a neural agricultural knowledge graph for structured reasoning and inference; (iii) an adaptive multi-agent reasoning system where AI agents specialize and collaborate across agricultural domains; and (iv) a causal transparency mechanism that ensures AI recommendations are interpretable, scientifically grounded, and aligned with real-world constraints. OpenAg aims to bridge the gap between scientific knowledge and the tacit expertise of experienced farmers to support scalable and locally relevant agricultural decision-making.