AI for Wildlife Lab

Advancing preservation of biodiversity with the help of AI

partners

Challenges per year

250+

AI for Wildlife Engineers

25,000+

hrs of Engineering

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WHAT IS AI FOR WILDLIFE LAB?

Multiplying AI Engineering Skills for Wildlife Protection

Labs by FruitPunch AI build a community of AI engineers with an ecosystem of partners around a specific topic and technology. AI for Wildlife Lab bundles the experience from all challenges into resources for machine learning projects zooming in on biodiversity protection. It helps its members and partners to strengthen their network with other organizations operating in the niche.

The Mission Goals Tech in Focus Challenges Results Participate

WHY AI LAB FOR WILDLIFE CONSERVATION?

A personal quest to protect wildlife

In the very beginning of the non-profit initiative, to become later FruitPunch AI, Buster Franken was looking for a way to make an impact in the real world. Buster put out a call to action to apply AI to solve problems related to the UN Sustainable Development Goals. The wildlife reserves of South Africa responded and weeks later, Buster was in the bush in Pilanesberg, SA to see how AI could help in their fight against poaching. He witnessed the trail of destruction left by poachers.

He knew he couldn’t do this alone so he recruited co-founder Sako Arts and 50 AI engineers from around the globe to tackle the beast. For 10 weeks, they worked closely together with the rangers to develop an autonomous drone with thermal cameras to detect poachers. This was the first AI for Good Challenge - AI for Wildlife.

Wildlife has a special place in our hearts and its conservation is an important part of the the UN Sustainable Goal #15 - Life of Land. With the AI for Wildlife Lab we’ll be able to do much more to protect it.

UN Sustainable Development Goal 15: Life on Land

Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.

Targets

Events

Publications

908

Actions

Find out more about Goal 15

Goal 15 infographic, source: unstats.un.org/sdgs/report/2022/

WHAT ARE THE GOALS OF THE LAB?

Build a community of skilled AI for Wildlife Engineers using machine learning to tackle wildlife conservation

Organize as many successful AI for Wildlife Challenges as possible

Build a network of partners active in the wildlife preservation niche and bundle their resources

Get the most out of the combined engineering effort by focusing on edge computing, computer vision and tiny ML technology

WHAT AI TECH DO WE FOCUS ON?

Edge Computing

When doing wildlife conservation in remote areas, any machine learning faces a lack of connectivity. You can’t connect to a server to compute object detection from the bush. The computer vision model has to run on the available hardware on edge. Edge computing in machine learning translates into energy-efficient models that are able to do more work independently, without being constantly plugged into the cloud.

Tiny ML

Closely tied to edge computing, tiny ML approaches try to create model miniatures that are not draining batteries or burning one GPUs. It’s very important for a battery-powered poacher-detecting drone flying over South African reserves with patchy connectivity. It tackles some fundamental challenges to machine learning - energy consumption, latency, storage and data security.

Computer Vision

Computer vision is a common use case in machine learning, but applying perception models on edge requires next level AI skills. Our AI for Wildlife engineers have to implement a working MLOps to improve poacher detection and figure out how to coexist with other edge MLs (like autonomous flight functions) under fairly tricky conditions.

Creating a machine learning model for an on-edge detection of poachers on thermal video feed of a wildlife protection drone.

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Completed

March 5, 2021

AI for Wildlife Challenge 1

Using AI to help protect wildlife in South Africa. Developing an edge-ready computer vision model to detect poachers on thermal video streams on a fixed wing drone.

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THE LAB RESULTS

We get some machine learning work done

Detect poachers on thermal vision on edge of a fixed wing drone
Do a wildlife sensus using computer vision models on 4k low light RGB aerial footage
Find patterns in poacher trace reports by using semi-supervised learning
Apply edge ML poacher detection on battery powered camera traps
Use audio perception models to identify elephants by their distinctive sounds
Develop prediction model for elephant migration based on collar GPS data
Automated operation of big game gates near wildlife reserves

SPOTS Robi Beninca launching the flying ranger UAV (unmanned aerial vehicle) & Poacher detection running on the thermal video camera footage on edge hardware of the UAV

Results: Watch and read

From our YouTube channel

From our Publications

November 20, 2023

From Pixels to Preservation: How AI Gives Rise to a Birdwatching Revolution

At the interface of ecology and the ever-increasing applications to use artificial intelligence for good, FruitPunch AI and the Swedish University of Agricultural Sciences (SLU) joined together to better understand how our ecosystems function. Over decades, passionate ornithologists have been taking thousands of images of birds across the world. In the AI for Eagles Challenge, we exploited this dataset to build a machine learning pipeline that would allow rapid assessment of a bird’s behavior, species, and age. Traditional manual labeling of such images is a time-consuming process, and the need for speed in assessing individual birds and population states underscores the urgency of the task. The successful implementation of AI has the potential to revolutionize the efficiency of bird image analysis. By automating the labeling process, the application can significantly accelerate the assessment of individual birds and contribute valuable insights into the broader state of bird populations.

August 25, 2023

Flying High with AI: Counting Pelican Breeding Pairs in the Danube Delta

Imagine flying in a small airplane over the vast wetlands of the Danube Delta on the shores of the Black Sea in Romania looking for patches of small white dots: great white pelicans (Pelecanus onocrotalus). While flying over the colonies researchers like Sebastian Bugariu from the Romanian Ornithological Society (ROS) take photos which will be used to count the number of breeding birds when back in the office. The number of breeding pairs has grown from ~5,000 pairs 15 years ago to recently ~18,000 pairs. Keeping good records of the breeding numbers is important but not an easy task. Back at the office, it can take weeks to go through the images and manually count the pelicans. Wouldn’t it be great if this process could be automated, freeing up time that could be dedicated to other important conservation? This is where the AI for Pelican challenge started.

August 10, 2023

Solving automated wildlife taxonomy with AI

In the wild landscapes of Europe, camera traps have become essential tools for ecologists and wildlife researchers, sharing glimpses into the lives of the continent's diverse and fascinating animals. These devices have revolutionized our understanding of European wildlife, but their use comes with a challenge - the sheer volume of data they generate. To tackle this issue, a team of dedicated FruitPunchers recently developed an AI solution for the European Wildlife Challenge.

July 27, 2023

Listening to the Giants: Protecting Forest Elephants Through Audio Monitoring

In the dense rainforests of Central Africa, a captivating endeavor is underway, driven by researchers from Cornell University. Their mission? To track and protect elephants through the power of audio monitoring. These majestic creatures emit resounding rumbles at an incredibly low frequency, nearly imperceptible to the human ear. These deep calls, traversing vast distances, serve as a concealed communication network for elephants, veiled from our understanding for centuries.

March 23, 2023

Understanding seals with AI

On 6th December 2022, the FruitPunchAI community joined forces with researchers from Colgate University to build a facial recognition model that enables the non-invasive study and monitoring of harbor seals and other marine mammals. The team’s goal was to build upon the existing SealNet model developed by Colgate University to improve its accuracy, generalizability across sea species and mammals, and increase development velocity across data processing and model development workflows.

August 29, 2022

AI-powered Wildlife Conservation in Africa

An account of 10-week teamwork developing multiple machine learning and hardware pipelines to bring production-ready AI to edge hardware on flying rangers.

August 28, 2022

Model Optimization and Pruning of Poacher-detecting YOLOv5

Optimizing a YOLOv5 model for NVIDIA Jetson Nano to increase the inference speed and reduce memory footprint, focusing on inference speed not the absolute mAP.

August 27, 2022

User-friendly, Wilderness-proof MLOps

Developing a CI/CD pipeline that automatically retrains the existing poacher-detecting model, which can be deployed to the drone with improved performance.

August 26, 2022

Autonomous Flight and the Landing of a Fixed-Wing UAV

Developing a model for autonomous landing using a ‘traditional’ technique using GPS beacons a more experimental method of Reinforcement Learning.

LLMs

Computer vision

NLP

Time Series Forecasting

FOR INDIVIDUALS

How can you participate?

Become a certified AI for Wildlife Engineer

Individual engineers who collaborate in the AI for Wildlife Challenges are trained in edge computing, computer vision, model pruning and other skills essential for the applied AI projects.

Sign-up at the platform for free and fill your profile. Pick your interests and join corresponding communities. Create a skill tree you’ll be developing with every AI for Good activity.

Start my journey

Upgrade your skills with AI bootcamps & masterclasses

Learn how to apply your acquired AI knowledge in the real world. Join AI for Good challenges and collaborate in teams all over the globe.

Upgrade my skills

Every activity adds to your development. You’ll be accredited with badges for specific hard and soft skills and certifications after an accomplished challenge.

Build my skill tree

AI for Wildlife Skills

Data Engineering

ML Pipelines

Quantization

Scrum

Reinforcement Learning

Tensorflow

Pruning

ONNX

Docker

Edge Computing

Data Augmentation

Object Detection

Unsupervised Methods

Kubernetes

FOR INDIVIDUALS

How can you participate?

Become a certified AI for Wildlife Engineer

Individual engineers who collaborate in the AI for Wildlife Challenges are trained in edge computing, computer vision, model pruning and other skills essential for the applied AI projects.

Join the community

Sign-up at the platform for free and fill your profile. Pick your interests and join corresponding communities. Create a skill tree you’ll be developing with every AI for Good activity.

Start my journey

Start your personalized learning journey

Participate in a challenge

Join our machine learning focused learning events led by experts collaborating with the AI for Good community. Learn with the pros and from one another.

Join the community platform

Get certified

Every activity adds to your development. You’ll be accredited with badges for specific hard and soft skills and certifications after an accomplished challenge.

Get certified

For Organizations

Solve your challenges!

Challenge Partners

Impact organizations and experts applying the results of the AI for Wildlife Challenges. Do you have a wildlife conservation problem that can be solved with AI? Submit your challenge

Contribute!

Contributing Partners

Use their teams, expertise and resources to advance AI for Wildlife Lab & Challenges. Can your experts or technology contribute to solving an AI for Wildlife Challenge? Partner up! 👇

Get your community involved!

Community Partners

Use their reach to spread the AI for Good news, advocate for AI for Good Lab & Challenges. Do you have a network we could reach out to with AI for Wildlife topics? Connect on Linkedin, Twitter or drop us a note 👇

Partners

Come on board the AI for Wildlife Lab!

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Advancing preservation of biodiversity with the help of AI

Multiplying AI Engineering Skills for Wildlife Protection

A personal quest to protect wildlife

UN Sustainable Development Goal 15: Life on Land

Edge Computing

Tiny ML

Computer Vision

AI for Bears

AI for Coral Reefs 2

AI for Turtles

AI for Eagles

AI for Pelicans

AI for European Wildlife

AI for Forest Elephants

AI for Seals Challenge

AI for Wildlife Challenge 3

AI for Wildlife Challenge 2

AI for Wildlife Challenge 1

We get some machine learning work done

From our YouTube channel

From our Publications

From Pixels to Preservation: How AI Gives Rise to a Birdwatching Revolution

Flying High with AI: Counting Pelican Breeding Pairs in the Danube Delta

Solving automated wildlife taxonomy with AI

Listening to the Giants: Protecting Forest Elephants Through Audio Monitoring

Understanding seals with AI

AI-powered Wildlife Conservation in Africa

Model Optimization and Pruning of Poacher-detecting YOLOv5

User-friendly, Wilderness-proof MLOps

Autonomous Flight and the Landing of a Fixed-Wing UAV

Become a certified AI for Wildlife Engineer

Become a certified AI for Wildlife Engineer

Join the community

Participate in a challenge

Get certified

Challenge Partners

Contributing Partners

Community Partners

Partners

Come on board the AI for Wildlife Lab!

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