CABBAGE

The dataset comprises over 500 images of cabbage (Brassica oleracea var. capitata) classified into two categories: "good" and "bad". These images were captured using a OnePlus Nord CE 2 Lite 5G mobile camera, under consistent daylight conditions against a black background.

Each image represents either a "good" cabbage, characterized by desirable attributes such as freshness, uniformity, absence of blemishes, and overall quality, or a "bad" cabbage, exhibiting defects like rot, discoloration, pest damage, or irregular shape.

The dataset provides a comprehensive representation of the variations in cabbage quality, facilitating the training and evaluation of machine learning models for cabbage classification tasks.

For each image, metadata may include details such as:
- Image resolution
- Image format (e.g., JPEG)
- Date and time of capture
- Lighting conditions
- Camera settings (e.g., aperture, exposure)
- Ground truth label (good/bad)

The images are labeled accordingly, enabling supervised learning algorithms to learn the distinguishing features between good and bad cabbage specimens. This dataset can be utilized across various applications, including agricultural automation, quality control in food processing industries, and computer vision research.