Brinjal Leaf Disease and Fruit Condition Image Dataset: A Field-Collected Dataset from Bangladesh

The 11 classes are: Aphids, Cercospora Leaf Spot, Defect Fruit, Early Blight, Healthy Fruit, Healthy Leaf, Magnesium Deficiency, Mosaic Virus, Phomopsis Blight, Phytophthora Blight, and Powdery Mildew. The images were collected from field locations in Bangladesh using multiple smartphone devices. The images were manually labeled and organized into class-specific folders.

All released images are standardized to 300 × 300 pixels and provided in image format. ### Folder Structure The dataset is organized under the parent folder **BrinjalDataset**, which contains two subfolders: RawDataset Number of images: 2,206 Number of classes: 11 Data format: .jpg AugmentedDataset Number of images: 8,800 Number of classes: 11 Images per class: 800 Data format: .jpg Both `RawDataset` and `AugmentedDataset` contain the same 11 class folders.

The original dataset preserves the natural class imbalance, while the augmented dataset provides a balanced distribution with 800 images per class. The dataset can be used for brinjal image classification, plant disease and symptom recognition, pest and nutritional disorder recognition, fruit condition classification, computer vision, deep learning, and agricultural image analysis. It is intended to support the development and evaluation of machine learning methods for automated brinjal condition recognition.