2-class Grapevine Pest Dataset of Scaphoideus titanus and Orientus ishidae on yellow Sticky traps for Insect Detection
DOI10.5281/zenodo.11441757Zenodo11441757MaRDI QIDQ6694131
Dataset published at Zenodo repository.
Alberto Gelmetti, Franca Ghidoni, Valerio Mazzoni, Paolo Sonego, Roberto Zorer, Giorgio Checola, Pietro Franceschi
Publication date: 21 June 2024
Copyright license: Creative Commons Attribution 4.0 International
This dataset consists of 615 images of Scaphoideus titanus (ST) and Orientus ishidae (OI) from yellow sticky traps (YST). Among these, 150 photos, which lack target insects, have been repurposed as background images. Insect annotations comprise 1329 ST and 1506 for OI, ensuring an almost class-balanced dataset. The images were acquired through four distinct methods: Photos from the field; Images of stored YST (T = 51C) and reared insects within a controlled greenhouse environment; Digital scans of YST collected during regular monitoring activities in the fields; Photos from a smart trap prototype installed in our experimental vineyard. Structure of the dataset, showing the number of images from each data source and the corresponding class annotations. Image source Number of images ST annotations OI annotations Number of background images Field 18 3 101 8 Laboratory 157 473 863 8 scanned 390 853 542 84 smart-trap 50 0 0 50 We provide the yellow sticky trap images already cropped in the pre-processing stage, the corresponding enhanced datasets focusing onbrightness contrast, sharpness, and a combination of both. Finally the annotations exported in YOLO format. Dataset structure crop/ bright/ sharp/ bright_and_sharp/ labels/ At the time of publication, this dataset is the largest publicly available resource in the control of FD vectors. Detailed documentation, along with model benchmarking and performance results is given in an accompanying journal paper: (paper under submission). Deployment You can use this dataset as starting point to train your own insect detection models. Open source Python scripts to deploy the trained models can be found in our Github repository.
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