Tree leaf Classification Dataset
Description
This dataset contains high-quality images of tree leaves from three species: Neem, Jujube, and Bougainvillea. Designed for machine learning and deep learning applications. The dataset is ideal for research in plant species identification.
Files
Steps to reproduce
1.Download the Dataset Access the dataset by downloading it from the Mendeley Data repository. 2.Unpack the Dataset Extract the dataset from the provided archive (e.g., .zip or .tar.gz) to a local directory. Directory Structure 3.Ensure the extracted dataset follows this structure: /dataset ├── Neem/ │ ├── image1.jpg │ ├── image2.jpg │ └── ... ├── Jujube/ │ ├── image1.jpg │ ├── image2.jpg │ └── ... └── Bougainvillea/ ├── image1.jpg ├── image2.jpg └── ... 4.Load the Dataset Use an image processing library such as Python's OpenCV, PIL, or a deep learning framework like TensorFlow or PyTorch to load the images. Example in Python: import os from PIL import Image base_dir = "/path/to/dataset" species = ["Neem", "Jujube", "Bougainvillea"] for sp in species: folder = os.path.join(base_dir, sp) for file in os.listdir(folder): image_path = os.path.join(folder, file) image = Image.open(image_path) # Perform any preprocessing or visualization 5.Preprocessing (Optional) Resize images to a consistent dimension if needed, e.g., 224x224 pixels, for models: 6.Organize the dataset for training, validation, and testing splits. 7.Train Models Use frameworks like TensorFlow, PyTorch, or Scikit-learn for building and training your models. 7.Evaluation Test the model on the testing dataset to evaluate performance (e.g., accuracy, precision, recall). 8.Visualize predictions and metrics (e.g., confusion matrix, training curves).