ArticleData in brief2023
CoLeaf-DB: Peruvian coffee leaf images dataset for coffee leaf nutritional deficiencies detection and classification.
Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- CoNutriNet: a dual-branch architecture with DenseNet and graph-enhanced attention network for coffee nutrient deficiency classification.Frontiers in plant science · 2026Article
- Harnessing Artificial Intelligence for Sustainable Agriculture: A Comprehensive Scientometric Review.Recent advances in food, nutrition & agriculture · 2026Review
- Classification of coffee leaf nutrient deficiencies using hybrid feature aggregation with hierarchical localized attention and MobileNet.Frontiers in artificial intelligence · 2026Article
- PND-Net: plant nutrition deficiency and disease classification using graph convolutional network.Scientific reports · 2024Article
- Coffee and cashew nut dataset: A dataset for detection, classification, and yield estimation for machine learning applications.Data in brief · 2024Article
- Mint leaves: Dried, fresh, and spoiled dataset for condition analysis and machine learning applications.Data in brief · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article introduces Peruvian coffee leaf datasets known as CATIMOR, CATURRA and BORBON of coffee plantations located at San Miguel de las Naranjas and La Palma Central, Jaén province, Cajamarca, Perú. The leaves with nutritional deficiencies were identified by agronomists, using a physical structure the controlled environment was designed and the images were captured with a digital camera. The dataset contains 1006 leaf images grouped according to their nutritional deficiencies (Boron, Iron, Potasium, Calcium, Magnesium, Manganese, Nitrogen and others). CoLeaf dataset contain images that facilitate training and validation during the utilization of deep learning algorithms for coffee plant leaf nutritional deficiencies recognition and classification. The dataset is publicly and freely available at http://dx.doi.org/10.17632/brfgw46wzb.1.
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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.