Cauliflower Dataset
Description
The dataset comprises images of cauliflower plants classified based on the various diseases attacking them. The dataset is meant for applications in plant disease classification and prediction methods. It is aimed at helping plant pathologists and farmers to identify the diseases affecting cauliflower at an early stage, thus preventing their occurrence. The dataset comprises of images of cauliflower and leaves which fall into six classes: A. Cauliflower Categories: 1. Alternaria Brassicae 2. Bacterial Soft Rot 3. Bacterial Spot 4. Black Rot 5. Healthy 6. Purple Tinges B. Leaf Categories: 1. Alternaria Leaf Spot 2. Downy Mildew 3. Healthy Images are collected from a variety of sources including publicly available agricultural datasets and field captures. The dataset has been preprocessed and labeled to ensure accurate classification.
Files
Steps to reproduce
Download the dataset from Mendeley and extract it. The dataset consists of images of cauliflower leaves classified into various disease classes. Use Python libraries like OpenCV or PIL to load and preprocess images. Split the dataset into train, validation, and test sets (e.g., 70%-20%-10%). Train a deep learning model, e.g., CNN using TensorFlow or PyTorch.