Abstract :

The rapid growth in botanical research and agricultural applications has increased the need for automated and accurate plant identification systems. Traditional manual plant identification methods are time-consuming, require expert knowledge, and are prone to human error. This project presents a web-based botanical plant identification system developed using the Flask framework integrated with a Convolutional Neural Network (CNN) model. The system allows users to upload plant leaf images and instantly receive plant species predictions with accuracy and reliability. The CNN model is trained using a curated dataset of plant leaf images and optimized through multiple training iterations to enhance performance. The Flask-based user interface ensures accessibility through any web browser, enabling users such as students, researchers, and farmers to benefit from the system. The proposed system demonstrates efficient processing, high recognition accuracy, and user-friendly interaction. Overal