JujubeBruiseNet: A high-resolution image dataset for bruise detection in Ziziphus mauritiana
Md Arham Tabib, Sumyia Sabrin Liza, Md Mizanur Rahman

TL;DR
JujubeBruiseNet is a high-resolution image dataset for detecting bruises in jujube fruits, aiming to improve post-harvest quality assessment.
Contribution
The novelty lies in providing a labeled dataset of jujube images for bruise detection, enabling automated quality control in agriculture.
Findings
The dataset contains 1464 high-resolution images of jujube fruits labeled as Healthy or Bruised.
Images were collected under controlled lighting to ensure uniformity and reduce external variations.
Abstract
The article presents JujubeBruiseNet, a high-resolution image dataset designed for bruise detection in Ziziphus mauritiana (jujube) fruits. Ziziphus mauritiana is a seasonal fruit often found in late summer to early fall. The bruise detection in this fruit is crucial for post-harvesting, fruit processing, and food packaging. Manual detection of bruises is time-consuming and often leads to inaccuracy. Therefore, developing a novel classification model is essential, which will immediately recognize bruises in the fruits and, as a result, decrease human effort, expenses, and production time in the agriculture sector. The dataset contains a total of 1464 original photos categorized by two classes labelled Healthy and Bruised. We collected the fruit from the local market and fields near Savar, Dhaka, Bangladesh, with the help of domain experts in the period from 10th March to 20th March…
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Taxonomy
TopicsSmart Agriculture and AI · Ziziphus Jujuba Studies and Applications · Spectroscopy and Chemometric Analyses
