Leaf image classification using deep learning
Leaf Image Classification Using Deep Learning
Detecting Leaf Type Based on Images Clicked Through Mobile Phone
Detecting Leaf Type Based on Images Clicked Through Mobile Phone
Introduction
The vast diversity of plant life on Earth makes it challenging to identify specific plant species. Traditional methods of plant identification rely on physical characteristics, such as leaf shape, size, and texture. However, these methods can be time-consuming and labor-intensive.
Deep learning has emerged as a powerful tool for image classification, offering a more efficient and accurate approach to plant identification. In this project, we explore the use of deep learning to classify leaf types based on images captured using a mobile phone.
Dataset
We utilize a dataset of leaf images collected from various sources, including online repositories and field surveys. The dataset encompasses a wide range of leaf types, ensuring the effectiveness of our classification model.
Data Preprocessing
Before applying deep learning techniques, we preprocess the leaf images to enhance their quality and consistency. This preprocessing involves resizing the images to a uniform size, normalizing pixel values, and applying data augmentation techniques to increase the variability of the dataset.
Deep Learning Model
We employ a convolutional neural network (CNN) as our deep learning model. CNNs are particularly adept at extracting features from images, making them well-suited for image classification tasks. Our CNN architecture consists of convolutional layers, pooling layers, and fully connected layers.
The vast diversity of plant life on Earth makes it challenging to identify specific plant species. Traditional methods of plant identification rely on physical characteristics, such as leaf shape, size, and texture. However, these methods can be time-consuming and labor-intensive.
Deep learning has emerged as a powerful tool for image classification, offering a more efficient and accurate approach to plant identification. In this project, we explore the use of deep learning to classify leaf types based on images captured using a mobile phone.
Dataset
We utilize a dataset of leaf images collected from various sources, including online repositories and field surveys. The dataset encompasses a wide range of leaf types, ensuring the effectiveness of our classification model.
Data Preprocessing
Before applying deep learning techniques, we preprocess the leaf images to enhance their quality and consistency. This preprocessing involves resizing the images to a uniform size, normalizing pixel values, and applying data augmentation techniques to increase the variability of the dataset.
Deep Learning Model
We employ a convolutional neural network (CNN) as our deep learning model. CNNs are particularly adept at extracting features from images, making them well-suited for image classification tasks. Our CNN architecture consists of convolutional layers, pooling layers, and fully connected layers.