FruitSeg30_Segmentation Dataset & Mask Annotations

Published: 17 June 2024| Version 3 | DOI: 10.17632/vkht8pfsp3.3


The “FruitSeg30_Segmentation Dataset & Mask Annotations” is a comprehensive collection of high-resolution images of various fruits, accompanied by precise segmentation masks. We structured this dataset into 30 distinct classes, which containing 1969 images and their corresponding masks, with each measuring 512×512 pixels. Each class folder contains two subfolders: “Images” with high-quality JPG images captured under diverse conditions and “Mask” with PNG files representing the segmentation masks. We meticulously collected the dataset from various locations in Malaysia, Bangladesh, and Australia, ensuring a robust and diverse collection suitable for training and evaluating image segmentation models like U-Net. This resource is ideal for automated fruit recognition and classification applications, agricultural quality control, and computer vision and image processing research. By providing precise annotations and a wide range of fruit types, this dataset serves as a valuable asset for advancing research and development in these fields.



Daffodil International University, Universiti Malaya, University of Southern Queensland


Artificial Intelligence, Computer Vision, Image Processing, Data Science, Image Segmentation, Fruit, Agricultural Development