TFDD: A Comprehensive 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 285 original images. This dataset is collected from a local tomato field in Bhashanchar, Faridpur, 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 -12° and +12°, shearing up to ±7° in both directions, brightness adjustments from -10% to +10%, and saturation variations between -7% and +7%. We augmented (3x) of the training images only. After augmentation we obtained 670 images (579 images for training, 58 images for validation, and 33 images for testing).