RGB Image Dataset of Urochloa Hybrids for High-Throughput Phenotyping and Artificial Intelligence Applications

Version 2.1

Arrechea-Castillo, Darwin Alexis; Espitia Buitrago, Paula Andrea; Florian Vargas, David Alberto; Estupinan Arboleda, Ronald David; Velazquez Hernandez, Riquelmer; Camelo Munevar, Rodrigo Andres; Cardoso Arango, Juan Andres, 2024, "RGB Image Dataset of Urochloa Hybrids for High-Throughput Phenotyping and Artificial Intelligence Applications", https://doi.org/10.7910/DVN/X4LM19, Harvard Dataverse, V2

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Description	This dataset represents an extended version of a previous work, accessible at this link: https://doi.org/10.7910/DVN/U0KL6Y. An additional 139 images and a total of 24,983 new annotations have been included. Combined with the original dataset, a total of 394 images with 47,323 annotations are now available. This new dataset differs from the previous one in several key ways, primarily in the conditions and types of images captured, as well as in the expanded annotations. In the initial release, lighting conditions were carefully controlled to standardize histogram distribution across all images. The images were also captured at a fixed distance and exclusively in a nadir (top-down) view, using a single sensor in a single geographic location. For this updated dataset, variability was prioritized across all aspects. Images were taken in multiple geographic locations, including Palmira, Colombia, and Ocozocoautla de Espinosa, Mexico. Different sensors were used, including a professional Nikon D5600 camera, smartphones (such as the Realme C53 and Oppo Reno 11), and even a Phantom 4 Pro V2 drone. The capture distance varied from 1 to 3 meters, resulting in images with differing spatial resolutions. Additionally, several capture angles were employed: no longer just nadir views but also oblique and frontal angles. Raceme density per plant was also increased. In the original dataset, the plant with the highest raceme count had 851 racemes. In the updated dataset, raceme counts reach as high as 1,586 in a similar area (~1m²), nearly doubling the count. This increase leads to a much higher degree of raceme overlap. This expanded dataset is expected to provide significant benefits for deep learning applications. The enhanced variability supports the development of more robust deep learning models, better suited to handle real-world diversity and complexity. (2024-11)
Subject	Earth and Environmental Sciences; Agricultural Sciences
Keyword	grasses, machine learning, high-throughput phenotyping, imagery, digital image processing, tropical agriculture, Americas, Crops for Nutrition and Health
License/Data Use Agreement	Custom Dataset Terms

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	01.ReadMe_RGB_ImageDatasetOfUrochloaHybridsForHTP_and_AI_applications.txt Plain Text - 1.0 KB Published Nov 13, 2024 22 Downloads MD5: cb960886e7ede1ac87fef276e5048631 Information Documentation	Preview "01.ReadMe_RGB_ImageDatasetOfUrochloaHybridsForHTP_and_AI_applications.txt" Access File File Access Public Download Options Plain Text Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
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Citation Metadata

Persistent Identifier	doi:10.7910/DVN/X4LM19
Publication Date	2024-11-13
Title	RGB Image Dataset of Urochloa Hybrids for High-Throughput Phenotyping and Artificial Intelligence Applications
Author	International Center for Tropical Agriculture - CIAThttps://orcid.org/0000-0002-2395-2181 International Center for Tropical Agriculture - CIAThttps://orcid.org/0000-0002-6610-1491 https://orcid.org/0009-0008-7582-0421 International Center for Tropical Agriculture - CIAThttps://orcid.org/0009-0000-1006-4401 https://orcid.org/0009-0004-4308-917X International Center for Tropical Agriculture - CIAThttps://orcid.org/0000-0003-0563-6123 International Center for Tropical Agriculture - CIAThttps://orcid.org/0000-0002-0252-4655
Point of Contact	Use email button above to contact. Alliance Data Management (Bioversity International and the International Center for Tropical Agriculture)
Description	This dataset represents an extended version of a previous work, accessible at this link: https://doi.org/10.7910/DVN/U0KL6Y. An additional 139 images and a total of 24,983 new annotations have been included. Combined with the original dataset, a total of 394 images with 47,323 annotations are now available. This new dataset differs from the previous one in several key ways, primarily in the conditions and types of images captured, as well as in the expanded annotations. In the initial release, lighting conditions were carefully controlled to standardize histogram distribution across all images. The images were also captured at a fixed distance and exclusively in a nadir (top-down) view, using a single sensor in a single geographic location. For this updated dataset, variability was prioritized across all aspects. Images were taken in multiple geographic locations, including Palmira, Colombia, and Ocozocoautla de Espinosa, Mexico. Different sensors were used, including a professional Nikon D5600 camera, smartphones (such as the Realme C53 and Oppo Reno 11), and even a Phantom 4 Pro V2 drone. The capture distance varied from 1 to 3 meters, resulting in images with differing spatial resolutions. Additionally, several capture angles were employed: no longer just nadir views but also oblique and frontal angles. Raceme density per plant was also increased. In the original dataset, the plant with the highest raceme count had 851 racemes. In the updated dataset, raceme counts reach as high as 1,586 in a similar area (~1m²), nearly doubling the count. This increase leads to a much higher degree of raceme overlap. This expanded dataset is expected to provide significant benefits for deep learning applications. The enhanced variability supports the development of more robust deep learning models, better suited to handle real-world diversity and complexity. (2024-11)
Subject	Earth and Environmental Sciences; Agricultural Sciences
Keyword	grasses (AGROVOC) http://aims.fao.org/aos/agrovoc/c_3362 machine learning (AGROVOC) http://aims.fao.org/aos/agrovoc/c_49834 high-throughput phenotyping (AGROVOC) http://aims.fao.org/aos/agrovoc/c_a0a81c10 imagery (AGROVOC) http://aims.fao.org/aos/agrovoc/c_36760 digital image processing (AGROVOC) http://aims.fao.org/aos/agrovoc/c_9000033 tropical agriculture (AGROVOC) http://aims.fao.org/aos/agrovoc/c_95589557 Americas (Research Region) Crops for Nutrition and Health (Research Lever)
Topic Classification	artificial intelligence (AGROVOC) http://aims.fao.org/aos/agrovoc/c_27064 agricultural sciences (AGROVOC) http://aims.fao.org/aos/agrovoc/c_49876 agronomy (AGROVOC) http://aims.fao.org/aos/agrovoc/c_211 imagery (AGROVOC) http://aims.fao.org/aos/agrovoc/c_36760 machine learning (AGROVOC) http://aims.fao.org/aos/agrovoc/c_49834
Language	English
Producer	Bioversity International and the International Center for Tropical Agriculture https://alliancebioversityciat.org/
Production Date	2024-03-01
Contributor	Data Collector: PAPALOTLA GROUP
Distributor	Bioversity International and the International Center for Tropical Agriculture https://alliancebioversityciat.org/
Depositor	Alliance Data Management
Deposit Date	2024-11-08
Time Period	Start Date: 2024-03-01; End Date: 2024-10-26
Date of Collection	Start Date: 2024-03-01; End Date: 2024-10-26
Data Type	Images; Experimental Data; Breeding Data
Related Material	Arrechea-Castillo, D.A., Espitia-Buitrago, P., Arboleda, R.D., Hernandez, L.M., Jauregui, R.N., Cardoso, J.A., 2024. High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids. Data Brief 57, 110928. https://doi.org/10.1016/j.dib.2024.110928
Related Dataset	Cardoso Arango, Juan Andres; Arrechea-Castillo, Darwin Alexis; Estupinan Arboleda, Ronald David; Escobar Graciano, Miller, 2024, "Top View RGB Image Dataset of Urochloa Hybrids for High-Throughput Phenotyping and Artificial Intelligence Applications", https://doi.org/10.7910/DVN/U0KL6Y, Harvard Dataverse, V2
Other Reference	Cardoso Arango, Juan Andres; Arrechea-Castillo, Darwin Alexis; Estupinan Arboleda, Ronald David; Escobar Graciano, Miller, 2024, "Top View RGB Image Dataset of Urochloa Hybrids for High-Throughput Phenotyping and Artificial Intelligence Applications", https://doi.org/10.7910/DVN/U0KL6Y, Harvard Dataverse, V2
Data Source	Darwin Alexis Arrechea-Castillo, Paula Espitia-Buitrago, Ronald David Arboleda, Luis Miguel Hernandez, Rosa N. Jauregui, Juan Andrés Cardoso, High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids, Data in Brief, Volume 57, 2024, 110928, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110928.

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