Journal Article

A multi-source fusion and feedback-optimized intelligent agent for crop disease and pest diagnosis and treatment

Xia, Yimin, He, Yuqing, Li, Fuzhong, Lv, Jia

2026

Overview

Crop diseases and pests pose a critical threat to global food security and agricultural sustainability. Traditional control methods are often limited by delayed diagnosis and a lack of capability for personalized solutions. To address these challenges, we developed a knowledge-enhanced diagnostic and treatment agent, optimized through multi-source knowledge fusion and farmer feedback. The agent integrates disease identification results from a visual model, multi-factor contextual parameters, and a crop knowledge graph. These components form a unified multi-source knowledge representation. The system converts multi-criteria farmer evaluations into reward signals, enabling continuous optimization of action strategies through interaction with real-world environments. Under the combined guidance of multi-source knowledge and farmer feedback-driven reinforcement learning, the agent can generate accurate and practically applicable treatment recommendations without additional task-specific fine-tuning of the generative model. Experiments on multiple baseline models demonstrate that combining these components consistently achieves the best performance. BERTScore increases by 25.23% on average, accuracy based on large language model evaluation improves by 30.27%, and practicality increases by 37.67%. These results validate the effectiveness and generalization capability of the proposed method.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Accuracy / Agricultural production / Control methods / Crop diseases / Crops / Datasets / Decision making / Diagnosis / farmer feedback / Farmers / Feedback / Food security / Health services / Identification / Intelligent agents / intelligent diagnosis and treatment / Knowledge / knowledge graph / Knowledge representation / Large language models / multi-source knowledge fusion / Multiple criterion / Original Research / Parameter identification / Pesticides / Pests / Plant diseases / reinforcement learning / Sustainable agriculture / Visual aspects