ArticleData in brief2022
VegNet: Dataset of vegetable quality images for machine learning applications.
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 25 papers.
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Who cites it
25 citing papers in PubMed.
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- Tamarind health assessment dataset: Images of shelled, unshelled, and mixed tamarind pods.Data in brief · 2025Article
- Nature's best vs. bruised: A veggie edibility evaluation database.Data in brief · 2025Article
- Dataset creation of thermal images of pomegranate for internal defect detection.Data in brief · 2025Article
- PlantCareNet: an advanced system to recognize plant diseases with dual-mode recommendations for prevention.Plant methods · 2025Article
- BanglaVeg: A curated vegetable image dataset from Bangladesh for precision agriculture.Data in brief · 2025Article
- A comprehensive hog plum leaf disease dataset for enhanced detection and classification.Data in brief · 2025Article
- Towards precision agriculture: A dataset for early detection of corn leaf pests.Data in brief · 2025Article
- Facilitating spice recognition and classification: An image dataset of Indian spices.Data in brief · 2024Article
- Multi-object urban dataset: A resource for detecting pedestrians, traffic and motorbikes.Data in brief · 2024Article
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- A comprehensive dataset of rice field weed detection from Bangladesh.Data in brief · 2024Article
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- A comprehensive dataset of eight Thai cannabis classes for botanical exploration.Data in brief · 2024Article
- A comprehensive lemongrass (Data in brief · 2024Article
- Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications.Data in brief · 2024Article
- Empowering agricultural research: A comprehensive custard apple (Data in brief · 2024Article
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The agricultural industry has an unmet requirement for quick and accurate classification or recognition of vegetables according to the quality criteria. This open research problem draws attention to the research scholars every time. The classification and object detection challenges have seen highly encouraging outcomes from machine learning and deep learning techniques. The foundational condition for developing precise and reliable machine learning models for the real-time context is a neat and clean dataset. With this goal in mind, we have developed a picture dataset of four popular vegetables in India that are also highly exported worldwide. In order to generate a dataset, we have taken into account four vegetables: Bell Peppers, Tomatoes, Chili Peppers, and New Mexico Chiles. The dataset is divided into four vegetable folders, including Bell Pepper, Tomato, Chili Pepper, and New Mexico Chile. Further each vegetable folder contains five subfolders namely (1) Unripe, (2) Ripe, (3) Old, and (4) Dried (5) Damaged. The image collection includes a total of 6850 pictures of vegetables in dataset. We firmly feel that the provided dataset is very beneficial for developing, evaluating, and validating a machine learning model for vegetable categorization or reorganization.
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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.