FruitSeg30_Segmentation Dataset & Mask Annotations: A Novel Dataset for Diverse Fruit Segmentation and Classification
Article
Article Title | FruitSeg30_Segmentation Dataset & Mask Annotations: A Novel Dataset for Diverse Fruit Segmentation and Classification |
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ERA Journal ID | 210363 |
Article Category | Article |
Authors | Shamrat, F.M. Javed Mehedi, Shakil, Rashiduzzaman, Idris, Mohd Yamani Idna, Akter, Bonna and Zhou, Xujuan |
Journal Title | Data in Brief |
Journal Citation | 56 |
Article Number | 110821 |
Number of Pages | 15 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 2352-3409 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.dib.2024.110821 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S2352340924007856 |
Abstract | Fruits are mature ovaries of flowering plants that are integral to human diets, providing essential nutrients such as vitamins, minerals, fiber and antioxidants that are crucial for health and disease prevention. Accurate classification and segmentation of fruits are crucial in the agricultural sector for enhancing the efficiency of sorting and quality control processes, which significantly benefit automated systems by reducing labor costs and improving product consistency. This paper introduces the “FruitSeg30_Segmentation Dataset & Mask Annotations”, a novel dataset designed to advance the capability of deep learning models in fruit segmentation and classification. Comprising 1969 high-quality images across 30 distinct fruit classes, this dataset provides diverse visuals essential for a robust model. Utilizing a U-Net architecture, the model trained on this dataset achieved training accuracy of 94.72 %, validation accuracy of 92.57 %, precision of 94 %, recall of 91 %, f1-score of 92.5 %, IoU score of 86 %, and maximum dice score of 0.9472, demonstrating superior performance in segmentation tasks. The FruitSeg30 dataset fills a critical gap and sets new standards in dataset quality and diversity, enhancing agricultural technology and food industry applications. |
Keywords | Fruit segmentation; Deep learning; Image classification; Dataset diversity; Data annotation; Computer vision; Fruit image; Agriculture automation |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
Byline Affiliations | University of Malaya, Malaysia |
Daffodil International University, Bangladesh | |
School of Business |
https://research.usq.edu.au/item/z9y80/fruitseg30-segmentation-dataset-mask-annotations-a-novel-dataset-for-diverse-fruit-segmentation-and-classification
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