Cotton Leaf Disease Dataset with Severity Levels
Description
This dataset comprises images of cotton leaves affected by various diseases, as well as healthy leaves. The data is meticulously organized into multiple folders, with each folder representing a specific disease or condition. The dataset includes images collected under diverse environmental conditions to enhance the robustness of machine learning models trained on it. The dataset is categorized as follows: Cotton_Healthy: Healthy cotton leaves without any visible signs of disease. Bacterial_Blight: Leaves showing symptoms of bacterial blight, characterized by dark, water-soaked spots that may enlarge over time. Fusarium_Wilt: Leaves affected by fusarium wilt, often exhibiting yellowing and wilting. Curl_Virus: Cotton leaves infected with curl virus, characterized by curling and deformation of leaves. The dataset is further stratified based on disease severity levels, including mild, moderate, severe, and critical stages. It is intended for training and validating machine learning models for automating disease detection and classification, enabling timely interventions in cotton farming.
Files
Steps to reproduce
1.Download the Dataset: Access the dataset by downloading the entire folder. The dataset is organized into multiple subfolders, each representing a specific cotton leaf disease. 2.Unzip the Dataset: Extract the contents of the downloaded file using any standard unzipping software. The dataset will be structured into folders named according to specific conditions: Cotton_Healthy, Bacterial_Blight, Fusarium_Wilt, and Curl_Virus. 3.Load the Images: Each folder contains images in .jpg format. These images can be loaded into image processing software or a programming environment such as Python (e.g., TensorFlow or PyTorch) for further analysis. 4.Preprocess the Data: Depending on the specific requirements of your machine learning model, preprocess the images. Common preprocessing steps include: 5.Resizing all images to a consistent size. Normalizing pixel values to a range of 0–1. Augmentation techniques such as flipping, rotation, and shearing to increase data diversity. Train a Machine Learning Model: Split the dataset into training and test sets. Use the training set to train your machine learning model for disease detection and classification. Ensure proper validation using the test set to assess model performance. 6.Evaluate the Model: Evaluate your model’s performance using standard metrics such as accuracy, precision, recall, and F1-score. These metrics will provide insights into how well your model detects and classifies specific cotton leaf diseases. 7.Reproduce Results: To reproduce the results, follow the exact steps used in your model’s training pipeline, including data preprocessing, model architecture, and evaluation procedures. Document each step clearly for reproducibility.