taissirboukrouba/Muti-Class-Plant-Classification-Using-ViT

Image classification of 41 different Algerian plants using Vision Transformers (ViT) # Multi-Class Plant Classification Using ViT - **Author(s):** Taissir Boukrouba - **Affiliation:** University of UHBC - **Date:** 07/2022 --- ## Table of Contents - Project Overview - Importance - Document Control - Installation - VIT Model Implementation - Methodology - I - Data Collection - II - Data Preparation And Modelling - Results - Predictions - Flutter App Implementation - Figma App Design - Heuristics - App Workflow - Project Limitations --- ## Project Overview This project focuses on the creation and testing of a personalized dataset consisting of 41 Algerian plant species. The primary goal is to apply state-of-the-art Vision Transformers (ViT), inspired by the Natural Language Processing (NLP) Transformer architecture, to achieve accurate multi-class image classification of these plants. Unlike traditional convolutional neural networks (CNNs), which rely on localized feature extraction, Vision Transformers offer a more global perspective, potentially enhancing classification accuracy in complex, multi-class datasets. By leveraging VITs and their self-attention mechanisms, this project seeks to explore the model’s ability to handle visual data, particularly in the context of distinguishing between multiple plant species.The trained model was integrated into a Flutter mobile application under the name of **"NEBTA"** , designed in Figma, to provide an easy-to-use interface for plant identification. This app uses the phone's camera, enabling real-time application of the Vision Transformer model to classify plant species directly from live images. ## Importance The multi-class classification of Algerian plants using Vision Transformers offers diverse practical applications. In agriculture, it can assist with an automated identification of crop species (crop monitoring) and can help in predicting crop yields by analyzing the health and growth stages of different plant species (Yield Prediction). For environmental management, it supports tracking ch …