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Data Brief
Data Brief
Data in Brief
2352-3409
Elsevier

S2352-3409(24)00821-7
10.1016/j.dib.2024.110857
110857
Data Article
Tolerance to spittlebugs (Aeneolamia varia) in Urochloa spp. and Megathyrsus maximus grasses: A dataset for plant damage phenotyping
Ruiz-Hurtado Andres Felipe a.f.ruiz@cgiar.org
anfruizhu@unal.edu.co
@afruizh
⁎
Espitia-Buitrago Paula @paulaespitia1

Hernandez Luis M.
Jauregui Rosa N.
Cardoso Juan Andres @grass_scientist

International Center for Tropical Agriculture (CIAT), A.A. 6713, Cali, Colombia
⁎ Corresponding author. a.f.ruiz@cgiar.organfruizhu@unal.edu.co@afruizh
22 8 2024
10 2024
22 8 2024
56 11085728 6 2024
12 8 2024
14 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
This dataset results from controlled experiments that assess the tolerance of Urochloa spp. and Megathyrsus maximus grasses to nymphal and adult spittlebug damage, particularly from Aeneolamia varia, which significantly impacts forage production in Neotropical regions. Data were collected under standardized conditions using high-throughput phenotyping methods, integrating image-capture techniques and analyses to ensure precise and consistent data acquisition. The dataset serves as a foundational resource for developing and validating computer vision models aimed at automated phenotyping, enabling accurate and high-throughput assessment of plant tolerance to spittlebug damage. Researchers can use the dataset to benchmark and compare different methodologies for plant damage assessment, fostering standardization and reproducibility in phenotyping studies.

Keywords

Brachiaria
Panicum
Machine learning
Computer vision
Infestation
Host-plant resistance
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pmcSpecifications TableSubject	Plant Science (General).	
Specific subject area	Breeding of Urochloa spp. and Megathyrsus maximus grasses in Latin America to spittlebug resistance, using computer vision for accurate tolerance assessment, increasing genetic gain rates.	
Type of data	Table, Image
Filtered table, Raw images.	
Data collection	Each experimental unit was placed in a white, enclosed chamber (dimensions: 1 m3), with a strip of LED day white lights (6000 k) for consistent illumination. Images were captured using a Canon 90D and a NIKON D7500 reflex cameras with the following set up: manual mode, focus mode AF-A single point, white balance set to 0.0 in the fluorescent mode, ISO speed set to 100, shutter speed set to 1/50 s, and aperture set to 5.6. All images were saved in both 14-bit RAW image file and high-quality JPEG formats.	
Data source location	Alliance of Bioversity International and CIAT in Palmira, Colombia (3°30′03.1″ N, 76°21′25.4″ W).	
Data accessibility	Repository name: “Dataset: Tolerance to spittlebugs (Hemiptera: Cercopidae) in Urochloa spp. and Megathyrsus maximus grasses”.
Data identification number: https://doi.org/10.7910/DVN/EGUVHA
Direct URL to data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EGUVHA
Instructions for accessing these data: Access the public available dataset URL, download the files, and follow the instructions in the README file to decompress the dataset and preserve the intended structure of folders and files.	

1 Value of the Data

• High-throughput phenotyping: The dataset provides 8318 high-resolution images for training and deploying computer vision models. These images capture various stages of spittlebug damage, allowing the development and refinement of machine learning algorithms/computer vision models for accurate phenotyping, which is essential for accelerating genetic gain in breeding programs.

• Standardized protocols: The data were collected using standardized phenotyping protocols to ensure consistency and reliability. This standardization reduces genotype-by-environment interaction and enables the comparison of results across different studies and breeding programs, facilitating broader applicability and validation of the findings.

• Diverse and comprehensive dataset: The images were captured under controlled conditions from multiple trials and organized based on treatment, spittlebug life stage, and damage extent. This comprehensive dataset covers a wide range of variations in plant damage, making it a robust resource for studying plant resistance and tolerance to spittlebugs.

• Open-access and reproducibility: This dataset is available for other researchers to use in validating and benchmarking their image-based methodologies for screening spittlebug resistance. This promotes transparency, reproducibility, and collaboration within the scientific community, ensuring that results can be compared and validated across different laboratories and studies.

• Integration with advanced technologies: The dataset is designed to be compatible with common computer vision models and data management frameworks. This integration with advanced technologies enables the development of automated workflows for plant damage assessment, reducing human bias and increasing the efficiency and accuracy of phenotyping processes.

2 Background

The dataset was generated within the context of Urochloa P. Beauv. (syn. Brachiaria (Trin.) Griseb.) and Megathyrsus maximus (Jacq.) B.K. Simon & S.W.L. Jacobs (syn. Panicum maximum Jacq.) breeding programs aimed at improving resistance and tolerance to spittlebugs. Urochloa spp. and M. maximus are widely used forage grasses in tropical and subtropical regions of America. Spittlebugs, particularly Aeneolamia varia (Fabricius, 1787), cause extensive damage to these grasses, affecting livestock productivity and the livelihoods of producers. For the development of new cultivars of Urochloa spp. and M. maximus with greater resistance to spittlebugs, deployment of high throughput phenotyping (HTP) methods is needed (c.f., [1,2]).

Currently, RGB images are used in HTP for accurately assessing plant resistance to spittlebugs [3]. Images are captured under controlled and standardized conditions and involves no-choice tests where spittlebugs are confined to individual plants in experimental units. This enables the assessment of plant damage under controlled infestation levels, and for the comparison of results across different studies.

3 Data Description

The dataset is organized into a hierarchical structure that includes multiple folders and subfolders, each containing specific data related to the assessment of spittlebug tolerance in Urochloa spp. and Megathyrsus maximus grasses. The main elements of the dataset are as follows:1. Main Folder:○ Contains the overall dataset structure with two main components: an Excel spreadsheet and a collection of JPG images.

2. Excel Spreadsheet:○ metadata.xlsx: This file lists all images in the dataset with their relative paths and attributes such as treatment, spittlebug life stage, damage extent, capture period, genotype, population, trial number, and year.

3. Image Collection Folder:○ Contains 8318 high-resolution JPG images (5568×3715 pixels) organized into subfolders based on specific attributes.

4. Subfolders within the Image Collection Folder:○ Treatment:▪ INFESTED: Images of plants infested with spittlebugs.

▪ UNINFESTED: Images of plants not infested with spittlebugs.

○ Stage:▪ ADULT: Images of plants infested with spittlebug adults captured at 0 days after infestation (dai), 7 dai and 14 dai.

▪ NYMPH: Images of plants infested with spittlebug nymphs captured at 0 dai and 35 dai.

○ Capture:▪ 7_days_after_start_of_treatment or 14_days_after_start_of_treatment for adult stage experiment captures.

▪ 0_days_after_start_of_treatment or 35_days_after_start_of_treatment for nymph stage captures.

○ Damage:▪ NO_DAMAGE: Images showing plants with no damage from spittlebugs.

▪ INTERMEDIATE_DAMAGE: Images showing plants with intermediate damage from spittlebugs.

▪ EXTENSIVE_DAMAGE: Images showing plants with extensive damage from spittlebugs.

5. Additional Subfolders and Files:○ Each main subfolder (e.g., INFESTED, UNINFESTED) contains further subfolders organized by stage, capture period, and damage classification.

○ Each image file is named following the convention: TAM_trial_year_capture_identifier.JPG, which includes the plant, its respective pot, and a color checker for reference.

The entire dataset structure allows for easy navigation and retrieval of specific images based on the experimental parameters and conditions under which they were captured (Fig. 1).Fig. 1 Dataset structure including folder names and number of files per folder with sample of the classified images based on plant damage.

Fig. 1

Fig. 2 Methodology of the trials for assessing spittlebug tolerance in Urochloa spp. and Megathyrsus maximus, and for plant damage quantification from digital images analysis.

Fig. 2

Fig. 3 Dataset generation methodology.

Fig. 3

4 Experimental Design, Materials and Methods

The dataset was acquired through a series of controlled experiments designed to assess the tolerance of Urochloa spp. and Megathyrsus maximus grasses to spittlebug (Aeneolamia varia) damage. This data is not derived from any primary research articles and does not form part of any other article. Table 1 provides a detailed description of the experimental design, methods, tools, instruments, and conditions used:Table 1 Materials and methods description.

Table 1Experimental Design (Fig. 2)	
Population Selection	Eight breeding populations of Urochloa spp. and Megathyrsus maximus grasses were selected from routine controlled experiments aimed at classifying genotypes for tolerance to nymphal and adult spittlebug damage.	
No-Choice Tests	Fifteen no-choice tests were conducted where spittlebugs were confined to individual plants. Each plant was infested with a predetermined number of nymphs or adults. Nymph trials: 6 spittlebug eggs per plant. Adult trials: 2 spittlebug individuals per plant.	
Growing Conditions	Experiments were conducted under greenhouse conditions to standardize environmental variables.	
Data Acquisition	
Image Capture	Each unit was placed in a white enclosed chamber (1 m3) with LED day white lights.
Cameras: Canon 90D and NIKON D7500 reflex cameras. Settings: Manual mode, AF-A single point focus, 0.0 white balance in fluorescent mode, ISO 100, shutter 1/50 s, aperture 5.6. Images saved in 14-bit RAW and high-quality JPEG formats.	
Timing of Image Capture	Before infestation (0 dai) and 35 dai for nymph trials; before infestation (0 dai) and at 7 and 14 dai for adult trials.	
Image Processing and Analysis (Fig. 2)	
Software Used	ImageJ for RGB image analysis based on color indices and clustering. Python scripts for data wrangling, cleaning, and extracting metadata.	
Data Organization	Images organized in folders based on treatment (INFESTED, UNINFESTED), stage (ADULT, NYMPH), capture period, and damage extent (NO_DAMAGE, INTERMEDIATE_DAMAGE, EXTENSIVE_DAMAGE). Naming convention: TAM_trial_year_capture_identifier.JPG.	
Tools and Instruments	
Lighting	LED day white lights (6000 K) for consistent illumination within enclosed chambers.	
Data Generation Workflow	
Pre-Infestation Setup	Experimental units prepared and placed in the enclosed chamber. Initial images captured before infestation (0 days).	
Post-Infestation Monitoring	Images captured at specific intervals (0, 7, 14, and 35 dai) to document damage progression.	
Data Compilation	All images and corresponding metadata compiled into the hierarchical dataset structure. Python scripts used to associate images with metadata and ensure data integrity (Fig. 3).	

Limitations

Not applicable.

Ethics Statement

Authors have read and follow the ethical guidelines for publishing. The present article does not include human subjects, animal experiments, or data obtained from social media platforms.

CRediT authorship contribution statement

Andres Felipe Ruiz-Hurtado: Conceptualization, Methodology, Data curation, Writing – original draft. Paula Espitia-Buitrago: Conceptualization, Methodology, Data curation, Writing – original draft. Luis M. Hernandez: Methodology, Resources, Writing – review & editing. Rosa N. Jauregui: Resources, Supervision, Writing – review & editing, Project administration, Funding acquisition. Juan Andres Cardoso: Conceptualization, Methodology, Supervision, Writing – review & editing, Project administration, Funding acquisition.

Data Availability

Tolerance to spittlebugs (Hemiptera: Cercopidae) in Urochloa spp. and Megathyrsus maximus grasses (Original data) (Dataverse).

Acknowledgments

We would like to thank Jeison Velasco, Santiago Vargas, Felix Pinzón and Maria Fernanda Zamora for their valuable contributions to the establishment of the trials and the data collection. This work was carried out as part of the CGIAR Initiatives: Accelerated Breeding and Sustainable Animal Productivity. We thank all donors who globally support our work through their contributions to the CGIAR System. The views expressed in this document may not be taken as the official views of these organizations. CGIAR is a global research partnership for a food-secure future. Its science is carried out by 15 Research Centers in close collaboration with hundreds of partners across the globe.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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