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Dataset . 2023
License: CC BY
Data sources: ZENODO
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https://doi.org/10.5281/zenodo...
Dataset . 2023
License: CC BY
Data sources: Sygma
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Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps

Authors: Ciampi, Luca; Zeni, Valeria; Incrocci, Luca; Canale, Angelo; Benelli, Giovanni; Falchi, Fabrizio; Amato, Giuseppe; +1 Authors

Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps

Abstract

The dataset The Pest Sticky Traps (PST) dataset is a collection of yellow chromotropic sticky trap pictures specifically designed for training/testing deep learning models to automatically count insects and estimate pest populations. Images were manually annotated by some experts of the Department of Agriculture, Food and Environment of the University of Pisa (Italy) by putting a dot over the centroids of each identified insect. Specifically, we labeled insects as belonging to the category “whitefly” considering two different species, i.e., the sweet potato whitefly (Bemisia tabaci) (Gennadius) and the greenhouse whitefly (Trialeurodes vaporariorum) (Westwood). The dataset comprises two subsets:- a subset we suggest using for the training/validation phases (contained in the `train/` folder)- a subset we suggest using for the test phase (contained in the `test/` folder) Annotations of the two subsets are contained in `train/annotations.csv` and `test/annotations.csv`, respectively. They have the following columns:- *imageName* - filename of the image containing the whiteflies,- *X,Y* - 2D coordinates of the whitefly in the image space,- *class* - class index of the insect (always 0 in this dataset). Citing our work If you found this dataset useful, please cite the following paper @inproceedings{CIAMPI2023102384, title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps}, journal = {Ecological Informatics}, volume = {78}, pages = {102384}, year = {2023}, issn = {1574-9541}, doi = {10.1016/j.ecoinf.2023.102384}, url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132}, year = 2023, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, } and this Zenodo Dataset @dataset{ciampi_2023_7801239, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}}, month = apr, year = 2023, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.7801239}, url = {https://doi.org/10.5281/zenodo.6560823} } Contact Information If you would like further information about the dataset or if you experience any issues downloading files, please contact us at luca.ciampi@isti.cnr.it

Keywords

Pest Monitoring, Pest Counting, Smart Agriculture, Object Counting, Insect Counting, Pest Density Estimation, Smart Farming, Pest Detection, Integrated Pest Management

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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