TFDD: A High-Quality Image Dataset for Accurate Tomato Fruit Disease Detection and Classification

Tomatoes are one of the most widely cultivated and economically significant crops worldwide. However, their productivity and quality are significantly affected by various diseases, including bacterial, viral, and fungal infections. Tomato Fruit Disease Detection (TFDD) dataset designed to support the development and evaluation of object detection models. The dataset contains 288 original images. The images were collected from a local tomato field in Bhashanchar, Faridpur, and a local market in Dhaka, Bangladesh . It consists of high-resolution images of tomatoes affected by different diseases, captured under natural field conditions such as Anthracnose, Blossom End Rot, Fruit Worm, Fruit Cracking, Late Blight, Mold, Early Blight, and Healthy. To enhance dataset diversity and improve model generalization, we applied several augmentation techniques, including horizontal and vertical flipping, rotations between -15° and +15°, brightness adjustments from -15% to +15%, and saturation variations between -20% and +20%. We augmented (3x) of the training images only. After augmentation we obtained 682 images (595 images for training, 58 images for validation, and 29 images for testing).

Dataset Overview:
Total Images: 682
Image Format: .jpg
Image Size: 640x640
Annotation Format: .txt
Number of Classes: 8 (Healthy and Diseased)

Data Sources and Annotation:
Field Location: Gafur Matubbor Dangi, Bhashanchar, Karirhat 7821, Sadarpur, Faridpur, Bangladesh
Local Market Location: Khilkhet, Dhaka, Bangladesh.
Captured Method: iPhone 13 and iPhone 15 Plus cameras
Annotation Tool: Roboflow (Manual process)
Annotation Guidelines: Bounding boxes
Annotation Format (.txt):

Class Distributions:
Class Name Raw Image count
Anthracnose 27
Blossom_end_rot 31
Cracking 35
Early_Blight 20
Fruitworm 61
Healthy 27
Late_Blight 38
Mold 49