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

This dataset comprises 8,318 images including Urochloa spp. and Megathyrsus maximus experimental units. The dataset is a resource for validating the current plant damage quantification technique for repeatability, and for training machine learning algorithms to identify, classify or quantify plant damage caused by biotic and abiotic stress with similar symptoms.<br><br> Methodology: These data were obtained from the assessment of 8 populations of the Urochloa and Megathyrsus maximus breeding programs in 15 no-choice tests. Each test had a row and column number to analyze the statistical data as a spatial design. The images were acquired prior to the infestation, and 35 days after the infestation for nymphs and prior to the infestation, 7 days after the infestation and 14 days after the infestations for adults. Each experimental unit was placed in a white, enclosed chamber (dimensions: 1x1x1 m), 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 1/50s, and aperture set to 5.6. The images included in the dataset are in JPEG format, the metadata for each image was compiled in a table after assessing the resistance to damage through visual and image-processing based methods. The files were organized in a folder-based image classification format (sometimes known as ImageNet) compatible with the one required by computer vision classification models.

搜集汇总

背景与挑战

背景概述

该数据集包含8,318张Urochloa spp.和Megathyrsus maximus草类的实验单元图像，旨在评估植物对spittlebugs（沫蝉）的耐受性。数据集通过标准化方法采集，包括在侵染前后不同时间点拍摄的高质量图像，并配有详细元数据，主要用于验证植物损伤量化技术的可重复性，以及训练机器学习算法以识别和分类由生物或非生物胁迫引起的植物损伤症状。