ArticleData in brief2022
An extensive dataset for successful recognition of fresh and rotten fruits.
Article in Data in brief, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Packed Fruits and Vegetables Visual Classification and Segmentation Benchmark.Scientific data · 2025Article
- JujubeBruiseNet: A high-resolution image dataset for bruise detection inData in brief · 2025Article
- Multi-Task NoisyViT for Enhanced Fruit and Vegetable Freshness Detection and Type Classification.Sensors (Basel, Switzerland) · 2025Article
- FruitVision: A benchmark dataset for fresh, rotten, and formalin-mixed fruit detection.Data in brief · 2025Article
- Dataset creation of thermal images of pomegranate for internal defect detection.Data in brief · 2025Article
- Enhancing fruit freshness classification with adaptive knowledge distillation and global response normalization in convolutional networks.PeerJ. Computer science · 2025Article
- Banana and Guava dataset for machine learning and deep learning-based quality classification.Data in brief · 2024Article
- Comprehensive watermelon disease recognition dataset.Data in brief · 2024Article
- Fruit freshness detection based on multi-task convolutional neural network.Current research in food science · 2024Article
- An extensive real-world in field tomato image dataset involving maturity classification and recognition of fresh and defect tomatoes.Data in brief · 2023Article
- A dataset for successful recognition of cucumber diseases.Data in brief · 2023Article
- A novel dataset of guava fruit for grading and classification.Data in brief · 2023Article
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Authors and funding
3 authors.
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Abstract
Detection of rotten fruits is very crucial for agricultural productions and fruit processing as well as packaging industries. Usually, the detection of fresh and rotten fruits is done manually which is an ineffective and lengthy process for farmers. For this reason, the development of a new classification model is required which will reduce human effort, cost, and production time in the agriculture industry by recognizing defects in the fruits. This article offers a major dataset to the researchers to develop effective algorithms for recognizing more variety of fruits and overcome the limitations by increasing accuracy as well as decreasing computation time. This dataset contains sixteen types of fruit classes, namely fresh grape, rotten grape, fresh guava, rotten guava, fresh jujube, rotten jujube, fresh pomegranate, rotten pomegranate, fresh strawberry, rotten strawberry, fresh apple, rotten apple, fresh banana, rotten banana, fresh orange, rotten orange. We collected various fresh and rotten fruit images from 16th to 31st March 2022 from different fruit shops and real fields with the help of a domain specialist from an agricultural organization. The dataset is hosted by the Department of Computer Science and Engineering, Jahangirnagar University, and is freely available at https://data.mendeley.com/datasets/bdd69gyhv8/1.
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