ArticleData in brief2022
An extensive sunflower dataset representation for successful identification and classification of sunflower diseases.
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 10 papers.
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Who cites it
10 citing papers in PubMed, 36 citations in OpenAlex.
- AMCD: A multi-domain agricultural crop and flower image dataset for deep learning-based classification.Data in brief · 2026Article
- A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.Journal of imaging · 2026Review
- Evaluation of deep learning models using explainable AI with qualitative and quantitative analysis for rice leaf disease detection.Scientific reports · 2025Article
- PriBeL: A primary betel leaf dataset from field and controlled environment.Data in brief · 2025Article
- A review of deep learning architectures for plant disease detection.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Applications of transfer learning in sunflower disease detection: advances, challenges, and future directions.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- A lightweight co-optimization model for field sunflower disease identification.Frontiers in plant science · 2025Article
- Empowering agricultural research: A comprehensive custard apple (Data in brief · 2024Article
- Citrus dataset for image classification.Data in brief · 2023Article
- An extensive dataset for successful recognition of fresh and rotten fruits.Data in brief · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sunflowers are agricultural seed crops that can be used for essential edible oils and ornamental purposes. This cash crop is primarily cultivated in North and South America. Sunflower crops are prone to various diseases, insects, and nematodes, resulting in a wide range of production losses. Digital image processing and computer vision approaches have been widely utilized to categorize and detect plant diseases including leaves, fruits, and flowers over the last few decades. Early diagnosis of infections in sunflowers helps to prevent them from spreading throughout the farm and reducing financial losses to the farmers. This article offers a resourceful dataset of sunflower leaves and flowers that will help the researchers in developing effective algorithms for the detection of diseases. The dataset contains healthy and affected sunflower leaves and flowers with downy mildew, gray mold, and leaf scars. The images were captured manually between 25
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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.