Algorithms in the orchard: An embedding-based expert answering system for apple rust

As sustainable agricultural practices gain importance, the need for intelligent pest control decision-making has grown. This paper introduces SEEDS: Similarity-based Expert Embedding Decision System, a Retrieval-Augmented Generation (RAG) based agricultural question-answering (QA) system. It is built upon a domain-specific knowledge graph (KG), representing Cedar Apple Rust disease, its host and causative agents, plant defense molecules against apple rust infection, and various pesticides. Utilizing the OpenAI embedding model, the system generates embeddings for user queries and KG data, employing similarity metrics to rank KG entries, facilitating accurate and relevant pest control recommendations. SEEDS is a promising niche AI tool in plant protection, setting the stage for scalable, extensible QA frameworks in precision agriculture. The results signify not only a step forward in agricultural expert systems but also highlight the potential for expanding this approach to other crops and pests, marking a substantial advancement in the use of AI for agricultural pest control. • Developed SEEDS: a RAG-based expert QA system for apple rust management. • Developed a plant protection KG with more than 184,000 nodes. • Combined embeddings and GPT models for precise pest control advice. • Explainable AI for transparency and trust in the system.