ArticleData in brief2024
CitrusUAT: A dataset of orange
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 5 papers.
What it found
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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
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Who cites it
5 citing papers in PubMed.
- Field-based and close-range multispectral imaging dataset for Huanglongbing (HLB) detection in orange trees: A resource for machine learning and digital agriculture.Data in brief · 2026Article
- Utilizing deep learning models for early detection and classification of fruit diseases: towards sustainable agriculture and enhanced food quality.Scientific reports · 2026Article
- A lightweight LBM detection transformer with multi-scale feature fusion for citrus-picking robots.Frontiers in plant science · 2026Article
- OptiNet-B3: a lightweight explainable deep learning model for multiclass classification of fruit and leaf diseases.Scientific reports · 2025Article
- YOLOV8-CMS: a high-accuracy deep learning model for automated citrus leaf disease classification and grading.Plant methods · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Around the world, citrus production and quality are threatened by diseases caused by fungi, bacteria, and viruses. Citrus growers are currently demanding technological solutions to reduce the economic losses caused by citrus diseases. In this context, image analysis techniques have been widely used to detect citrus diseases, extracting discriminant features from an input image to distinguish between healthy and abnormal cases. The dataset presented in this article is helpful for training, validating, and comparing citrus abnormality detection algorithms. The data collection comprises 953 color images taken from the orange leaves of
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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.