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.