MangoImageBD: An extensive mango image dataset for identification and classification of various mango varieties in Bangladesh
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View abstract on PubMed
Summary
This summary is machine-generated.A new mango image dataset features 15 Bangladeshi varieties, aiding computer vision and agricultural applications. This collection supports automated mango identification, quality assessment, and biodiversity research for improved food security.
Area Of Science
- Computer Vision
- Agriculture
- Biodiversity Research
Background
- Mango (Mangifera indica) is a significant crop in Bangladesh, with diverse popular varieties.
- Accurate identification and assessment of mango varieties are crucial for agriculture, food processing, and biodiversity studies.
- Existing datasets may lack comprehensive coverage of regional mango varieties and sufficient augmentation for machine learning.
Purpose Of The Study
- To create a comprehensive and diverse image dataset of fifteen popular Bangladeshi mango varieties.
- To facilitate the development and training of machine learning and deep learning models for computer vision tasks related to mangoes.
- To support applications in precision agriculture, food supply chain management, and biodiversity conservation.
Main Methods
- Collected mango images from six major fruit-producing districts in Bangladesh.
- Captured high-definition images under standardized conditions using smartphone cameras.
- Processed and augmented the image dataset (28,515 images total) with various transformations to enhance model robustness.
Main Results
- A dataset of 28,515 images, including raw, processed, and augmented images of 15 mango varieties.
- The dataset comprises 5,703 original images and 17,109 augmented images.
- Images were sourced from diverse geographic locations to ensure wide representation.
Conclusions
- The created mango image dataset is valuable for computer vision applications in agriculture, such as automated variety identification, sorting, and quality assessment.
- This resource can aid in breeding climate-resilient mango varieties, enhancing food security and sustainable farming practices.
- The dataset contributes to phenotypic diversity studies, traceability, and the conservation of unique mango varieties.
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