ArticleAnimals : an open access journal from MDPI2023
Animal Species Recognition with Deep Convolutional Neural Networks from Ecological Camera Trap Images.
Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07658885 (Development, Validation and Clinical Efficacy Evaluation of an AI-based Snake Species Identification System for Snakebite Treatment), which is not on this map. Cited by 10 papers.
What it found
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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.
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.
Development, Validation and Clinical Efficacy Evaluation of an AI-based Snake Species Identification System for Snakebite Treatment: a Multicenter Prospective Observational Study
Who cites it
10 citing papers in PubMed.
- HST-former: hierarchical spatio-temporal aggregation for video-based animal re-identification.Scientific reports · 2026Article
- EyeInvaS: Lowering Barriers to Public Participation in Invasive Alien Species Monitoring Through Deep Learning.Animals : an open access journal from MDPI · 2025Article
- Multi-Modality Sheep Face Recognition Based on Deep Learning.Animals : an open access journal from MDPI · 2025Article
- Snake-DETR: a lightweight and efficient model for fine-grained snake detection in complex natural environments.Scientific reports · 2025Article
- A mobile hybrid deep learning approach for classifying 3D-like representations of Amazonian lizards.Frontiers in artificial intelligence · 2025Article
- A Serial Multi-Scale Feature Fusion and Enhancement Network for Amur Tiger Re-Identification.Animals : an open access journal from MDPI · 2024Article
- Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches.Sensors (Basel, Switzerland) · 2024Article
- An Improved Method for Broiler Weight Estimation Integrating Multi-Feature with Gradient Boosting Decision Tree.Animals : an open access journal from MDPI · 2023Article
- From beasts to bytes: Revolutionizing zoological research with artificial intelligence.Zoological research · 2023Review
- Monitoring Endangered and Rare Wildlife in the Field: A Foundation Deep Learning Model Integrating Human Knowledge for Incremental Recognition with Few Data and Low Cost.Animals : an open access journal from MDPI · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate identification of animal species is necessary to understand biodiversity richness, monitor endangered species, and study the impact of climate change on species distribution within a specific region. Camera traps represent a passive monitoring technique that generates millions of ecological images. The vast numbers of images drive automated ecological analysis as essential, given that manual assessment of large datasets is laborious, time-consuming, and expensive. Deep learning networks have been advanced in the last few years to solve object and species identification tasks in the computer vision domain, providing state-of-the-art results. In our work, we trained and tested machine learning models to classify three animal groups (snakes, lizards, and toads) from camera trap images. We experimented with two pretrained models, VGG16 and ResNet50, and a self-trained convolutional neural network (CNN-1) with varying CNN layers and augmentation parameters. For multiclassification, CNN-1 achieved 72% accuracy, whereas VGG16 reached 87%, and ResNet50 attained 86% accuracy. These results demonstrate that the transfer learning approach outperforms the self-trained model performance. The models showed promising results in identifying species, especially those with challenging body sizes and vegetation.
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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.