Insects feeding on xylem sap, such as adult Aphrophoridae spittlebugs, are vectors of the plant pathogenic xylem-limited bacterium Xylella fastidiosa (Xf), a causal agent of a number of severe diseases, including the Olive Quick Decline Syndrome (OQDS), which has decimated olive trees in the Mediterranean region. The Aphrophoridae life cycle and behaviour feature a weak stage, known as the juvenile stage, in which the insects live solitary on stems covered in a self-produced foamy fluid (froth) that protects them from dehydration and temperature stress. Juvenile vectors are ideal targets for a control intervention aimed at reducing transmission by adults. This paper presents the first, to the best of our knowledge, image dataset framing spittlebug froth samples in the field for the purpose of automated Aphrophoridae nymph identification. Images were captured using different devices including a consumer-grade RGB-D sensor, a digital reflex camera, and a smartphone camera. The dataset comprises 365 colour images, focusing on spittlebug foam. 211 of these images were captured in April 2024 during a two-day campaign. For these 211 images, a manual semantic annotation was performed, generating PNG binary masks that precisely distinguish spittlebug foam pixels from the background. To further enhance usability, labels are also provided in YOLO (You Only Look Once) format as text files, both for segmentation and object detection. The remaining 154 images were collected during a separate two-day campaign in 2025. These images are unannotated and are intended for further testing purposes. Overall, the dataset enables the development of both semantic segmentation models and object detectors for automated froth detection in natural images, thus facilitating the early identification of potentially harmful insects in sustainable pest management and control systems.
Keywords: Deep learning; Image annotation; Image segmentation; Pest management; Spittlebug foam detection.
© 2026 The Author(s).