Dataset Information

CoLeaf-DB: Peruvian coffee leaf images dataset for coffee leaf nutritional deficiencies detection and classification.

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.

SUBMITTER: Tuesta-Monteza VA

PROVIDER: S-EPMC10293970 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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CoLeaf-DB: Peruvian coffee leaf images dataset for coffee leaf nutritional deficiencies detection and classification.

Tuesta-Monteza Victor A VA Mejia-Cabrera Heber I HI Arcila-Diaz Juan J

Data in brief 20230512

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, ...[more]

PMID: 37383801

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Project description:The real-time detection of multinational banknotes remains an ongoing research challenge within the academic community. Numerous studies have been conducted to address the need for rapid and accurate banknote recognition, counterfeit detection, and identification of damaged banknotes [1], [2], [3]. State-of-the-art techniques, such as machine learning (ML) and deep learning (DL), have supplanted traditional digital image processing methods in banknote recognition and classification. However, the success of ML or DL projects critically hinges on the size and comprehensiveness of the datasets employed. Existing datasets suffer from several limitations. Firstly, there is a notable absence of a Peruvian banknote dataset suitable for training ML or DL models. Second, the lack of annotated data with specific labels and metadata for Peruvian currency hinders the development of effective supervised learning models for banknote recognition and classification. Lastly, datasets from different regions may not align with the unique characteristics, design, and security features of Peruvian banknotes, limiting the accuracy and applicability of models in a Peruvian context [4] To address these limitations, we have meticulously curated a comprehensive dataset comprising a total of 9,315 images of Peruvian banknotes, encompassing both old and new denominations from 2011 (old) and 2019 (new) [5]. The Peruvian banknote dataset includes denominations of 10, 20, 50, and 100 Peruvian soles. Importantly, as indicated by [5], both the 2011 and 2019 families of banknotes are currently in circulation, further enhancing the dataset's relevance for real-world applications in currency recognition and verification. This dataset serves as a vital resource for addressing the challenges in real-time multinational banknote detection. By offering a comprehensive collection of images of Peruvian banknotes, both old and new, this dataset fills a critical gap in the field of banknote recognition. Researchers can utilize it to train and evaluate advanced machine learning and deep learning models, ultimately enhancing the accuracy of banknote processing systems.

Dataset Information

CoLeaf-DB: Peruvian coffee leaf images dataset for coffee leaf nutritional deficiencies detection and classification.

Publications

CoLeaf-DB: Peruvian coffee leaf images dataset for coffee leaf nutritional deficiencies detection and classification.

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