ArticleData in brief2025
FruitVision: A benchmark dataset for fresh, rotten, and formalin-mixed fruit detection.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- SpiceSpectrum: Class-balanced dataset of commercially valuable spice cultivars.Data in brief · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a large-scale, meticulously validated dataset designed for the classification of fruit quality across three critical categories: fresh, rotten, and formalin-mixed. This dataset provides a comprehensive collection of five fruit types-apple, banana, mango, orange, and grapes-classified into three categories. Ensuring fruit safety is essential, especially in detecting formalin-mixed samples. Formalin is a common preservative used to extend shelf life, but it poses serious health risks. The dataset was collected from April 18, 2024, to July 31, 2024, using three different mobile cameras to introduce diversity in image quality and conditions. Initially, the dataset comprised 10,154 images. To enhance its robustness and applicability, eight data augmentation techniques-rotation (i.e., 45, 60, 90 degree), horizontal flipping, zooming, brightness adjustment, shearing, and Gaussian noise addition-were applied, increasing the total number of images to 81,232. All images were uniformly resized to 512 × 512 pixels to ensure consistency and facilitate integration with machine learning and computer vision models. This dataset supports a variety of applications, including automated fruit classification, food quality control, and real-time monitoring through IoT technology. It offers valuable resources for research and development in automated detection systems, improving food safety and processing efficiency, and addressing the health risks associated with formalin exposure.
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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.