Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases
Liqiong Wang, Teng Jin, Jinyu Yang, Ales Leonardis, Fangyi Wang, Feng, Zheng

TL;DR
Agri-LLaVA is a multimodal assistant tailored for agriculture, leveraging a large domain-specific dataset and knowledge-infused training to improve pest and disease identification and management.
Contribution
The paper introduces the first agricultural multimodal dataset and a knowledge-infused training method for developing specialized LMMs in agriculture.
Findings
Agri-LLaVA outperforms existing models in agricultural multimodal tasks.
The dataset covers over 221 pest and disease types with 400,000 entries.
The system demonstrates strong visual understanding and conversation capabilities in agriculture.
Abstract
In the general domain, large multimodal models (LMMs) have achieved significant advancements, yet challenges persist in applying them to specific fields, especially agriculture. As the backbone of the global economy, agriculture confronts numerous challenges, with pests and diseases being particularly concerning due to their complexity, variability, rapid spread, and high resistance. This paper specifically addresses these issues. We construct the first multimodal instruction-following dataset in the agricultural domain, covering over 221 types of pests and diseases with approximately 400,000 data entries. This dataset aims to explore and address the unique challenges in pest and disease control. Based on this dataset, we propose a knowledge-infused training method to develop Agri-LLaVA, an agricultural multimodal conversation system. To accelerate progress in this field and inspire…
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Taxonomy
TopicsSmart Agriculture and AI
