AutoPollS: a tool for automated monitoring of pollinators using deep learning

Zenodo (CERN European Organization for Nuclear Research) 2026 Open access

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Authors: Matthew A.-Y. Smith, Olivia M. Bernauer, Grace Melone, Brett Graham, Julia Wiessing, Rafael Salas, Jeremy Hemberger, Abhishek Bhattacharyya, Acacia T. S. Tang, Elena Rojas, Joshua San Miguel, Brian J. Spiesman
+2 more Claudio Gratton, James D. Crall
DOI: 10.5281/zenodo.19360800
Cited by: 1

Abstract

AutoPollS: a tool for automated monitoring of pollinators using deep learning Within the ZIP provided is the following: AutoPollS on-edge software for Raspberry Pi the Offline Analysis pipeline: to install and operate the Offline Analysis pipeline please read and follow the instructions in the README.md file. Manuscript code: to recreate the figures within the manuscript. The README contains details on the full image datasets collected and analyzed within the manuscript, hosted via AWS: Sunflower: https://autopolls-mee-manuscript-data-2026.s3.us-east-2.amazonaws.com/sunflowerData.zip Alpine Meadow: https://autopolls-mee-manuscript-data-2026.s3.us-east-2.amazonaws.com/colorado_images.zip Apple Orchard: https://autopolls-mee-manuscript-data-2026.s3.us-east-2.amazonaws.com/appleData_2022_v1.zip Follow the autopolls.ai website for latest updates and code and usage. Enjoy! Corresponding authors: Matthew Smith - msmith84[at]illinoistech.edu James Crall - james.crall[at]wisc.edu

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
1 April 2026