ArticleEcology and evolution2024
Using convolutional neural networks to count parrot nest-entrances on photographs from the largest known colony of Psittaciformes.
Article in Ecology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Using next-generation sequencing to unravel the diet composition of Burrowing Parrots over the annual cycle.Die Naturwissenschaften · 2026Article
- Using convolutional neural networks to count parrot nest-entrances on photographs from the largest known colony of Psittaciformes.Ecology and evolution · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Counting animal populations is fundamental to understand ecological processes. Counts make it possible to estimate the size of an animal population at specific points in time, which is essential information for understanding demographic change. However, in the case of large populations, counts are time-consuming, particularly if carried out manually. Here, we took advantage of convolutional neural networks (CNN) to count the total number of nest-entrances in 222 photographs covering the largest known Psittaciformes (Aves) colony in the world. We conducted our study at the largest Burrowing Parrot
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