Image recognition algorithms for jaguar detection and identification

Published: 22 September 2022
Last edited: 12 September 2023

The image recognition component, based on Convolutional Neural Networks (CNN) in the framework of the Tech4Nature Mexico pilot, plays a fundamental role by: i) Automatically detecting the presence of jaguars in camera trap captures, thus speeding up data processing; and ii) Automatically identifying individual jaguars in the region, which enhances the understanding of local populations. This approach is of vital importance in conservation by taking advantage of advanced methods that allow a faster and more detailed analysis.


Evaluation, effectiveness measures and learning
Technical interventions and infrastructure
Scale of implementation
Phase of solution

Enabling factors

Data collected from the devices and strategic alliances with Huawei, UPY, and other expert conservation organizations for data sharing. During a full school year, a group of young data engineering students from UPY were dedicated to the development of the image recognition models. Given the possibility that the models could be biased in the recognition of jaguars due to the students' lack of experience in monitoring this species, the group received training and feedback from a biologist specialized in feline conservation in Yucatan.

Lessons learned

We faced a considerable challenge in developing automatic models for the detection and identification of jaguars in images. This task is complicated not only by the scarcity of available data, but also by the limited amount of images captured by camera traps containing the species of interest, due to its critical conservation status. These obstacles have been notable in the initial stages of the project, prompting us to collect animal images from a variety of sources to expand our dataset. However, complexity increases at this stage due to additional factors.

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