DeepLeaf: Plant Species Classification Using Leaf Images and GPS Data with Convolution Neural Network

In the realm of botanical research, understanding and classifying plant species are fundamental tasks with vast ecological and conservation implications. “DeepLeaf” emerged as a groundbreaking project that leverages deep learning, mainly convolutional neural networks (CNNs), to revolutionize plant species identification and geographical mapping. The core of this project involves a multi-step approach. The initial phase centers on the collection of extensive datasets comprising high-resolution leaf images and their corresponding GPS coordinates from various geographic locations. These datasets provide the foundation for training and validating our deep learning model. The heart of the project lies in the implementation of CNNs, which excel in feature extraction from visual data. The model learns to recognize intricate patterns in leaf structures, enabling precise plant species classification. What sets “DeepLeaf” apart is the integration of GPS location data. This addition not only bolsters classification accuracy but also adds a geospatial dimension to the project. The incorporation of location data allows for insights into the geographical distribution of plant species, contributing to ecological and environmental research. “DeepLeaf” represents a vital tool for a wide range of professionals and enthusiasts, including ecologists, botanists, and conservationists. This project streamlines plant species identification, facilitating rapid, and accurate cataloging. Furthermore, it yields data that informs conservation efforts, biodiversity research, and ecological studies by unveiling geographical distribution patterns. It stands as a pioneering project with the potential to reshape how we identify and understand plant species while offering valuable insights into their geographic prevalence.