Plant Identification and Query System Using Deep Learning and RAG-Based Search

In this project, we present a dual-module system that utilizes a Convolutional Neural Network (CNN) for leaf-based plant identification and integrates a Retrieval-Augmented Generation (RAG) system for answering plant-related queries. The CNN was trained and fine-tuned using the LeafSnap dataset comprising 184 plant classes, achieving a final accuracy of 88%. Additionally, the RAG module leverages LangChain tools incorporating Wikipedia, Arxiv, and a custom PDF retriever for context-aware plant information search. This end-to-end solution is implemented using Flask and Streamlit, offering users a seamless interface to either upload plant leaf images or ask questions.