ArticleData in brief2024
A comprehensive dragon fruit image dataset for detecting the maturity and quality grading of dragon fruit.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 32 citations in OpenAlex.
- Image-Based Segregation of High-Quality Dragon Fruits Among Ripe Fruits.Sensors (Basel, Switzerland) · 2026Article
- JujubeBruiseNet: A high-resolution image dataset for bruise detection inData in brief · 2025Article
- Dataset creation of thermal images of pomegranate for internal defect detection.Data in brief · 2025Article
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- UDCAD-DFL-DL: A unique dataset for classifying and detecting agricultural diseases in dragon fruits and leaves.Data in brief · 2025Article
- A data-driven approach to turmeric disease detection: Dataset for plant condition classification.Data in brief · 2025Article
- Lentil plant disease and quality assessment: A detailed dataset of high-resolution images for deep learning research.Data in brief · 2025Article
- Article
- FruitSeg30_Segmentation dataset & mask annotations: A novel dataset for diverse fruit segmentation and classification.Data in brief · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dragon fruit, often referred to as pitaya, is a tropical fruit with various types, including both white-fleshed and red-fleshed varieties. Its distinctive appearance is complemented by a range of potential health advantages. These include its abundance of nutrients and antioxidants, which contribute to a robust immune system, aid in blood sugar regulation, and support the well-being of the heart, bones, and skin. Consequently, the global desire for dragon fruit is yielding substantial economic advantages for developing nations like Bangladesh, which in turn underscores the pressing need for an automated system to identify the optimal harvest time and differentiate between fresh and defective fruits to ensure quality. To accomplish this objective, this paper introduces an extensive collection of high-resolution dragon fruits because effective detection by machine learning models necessitates a substantial amount of data. The dataset was painstakingly gathered during a span of four months from three distinct locations in Bangladesh, with the valuable assistance of domain experts. Possible application of the dataset encompasses quality evaluation, robotic harvesting, and packaging systems, ultimately boosting the effectiveness of dragon fruit production procedures. The dataset has the potential to be a valuable resource for researchers interested in dragon fruit cultivation, offering a solid foundation for the application of computer vision and deep learning methods in the agricultural industry.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.