Tomato Leaf Disease Dataset
Description
This dataset consists of images of tomato leaves affected by various diseases. The data is organized into multiple folders, each corresponding to a different disease affecting tomato leaves. The images were collected under various conditions and are intended for use in training machine learning models for disease detection and classification. This dataset comprises images of tomato leaves categorized into different folders based on the specific disease affecting them. The folders include: 1. Tomato_healthy: Healthy tomato leaves without any signs of disease. 2. Bacterial_spot: Leaves affected by bacterial spot disease. 3. Early_blight: Leaves showing symptoms of early blight. 4. Late_blight: Leaves with signs of late blight. 5. Leaf_Mold: Tomato leaves affected by leaf mold. 6. Septoria_leaf_spot: Leaves showing septoria leaf spot disease. 7. Spider_mites_Two-spotted_spider_mite: Leaves infested with two-spotted spider mites. 8. Tomato_mosaic_virus: Leaves infected with tomato mosaic virus. 9. Target_Spot: Leaves showing target spot disease. 10. Tomato_Yellow_Leaf_Curl_Virus: Leaves affected by tomato yellow leaf curl virus
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 tomato 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 the specific diseases: Tomato_healthy, Bacterial_spot, Early_blight, Late_blight, Leaf_Mold, Septoria_leaf_spot, Spider_mites_Two- spotted_spider_mite, Tomato_mosaic_virus, Target_Spot, and Tomato_Yellow_Leaf_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 (TensorFlow) for further analysis. 4. Preprocess the Data: Depending on the specific requirements of your machine learning model, you may need to preprocess the images. Common preprocessing steps include resizing, normalization, and augmentation (e.g., flipping, rotation, shearing). 5. 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 that your model is appropriately validated using the test set. 6. Evaluate the Model: Evaluate the performance of your model using standard metrics such as accuracy, precision, recall, and F1-score. These metrics will help you understand how well your model performs in detecting the specific tomato leaf diseases. 7. Reproduce Results: To reproduce the results, follow the exact steps used in your model’s training, including the preprocessing, model architecture, and evaluation procedures.