ArticleSensors (Basel, Switzerland)2021
Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Large-Scale Coastal Marine Wildlife Monitoring with Aerial Imagery.Journal of imaging · 2025Article
- CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement.Sensors (Basel, Switzerland) · 2025Article
- Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence.Database : the journal of biological databases and curation · 2024Article
- An efficient visual servo tracker for herd monitoring by UAV.Scientific reports · 2024Article
- Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys.Scientific reports · 2023Article
- Cow Behavioural Activities in Extensive Farms: Challenges of Adopting Automatic Monitoring Systems.Sensors (Basel, Switzerland) · 2023Review
- Evidence on the efficacy of small unoccupied aircraft systems (UAS) as a survey tool for North American terrestrial, vertebrate animals: a systematic map.Environmental evidence · 2023Article
- Towards the fully automated monitoring of ecological communities.Ecology letters · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.