Monday, August 29, 2022
Edge Computing
Edge Computing
AI for Wildlife
AI for Wildlife
TinyML
TinyML

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.

Scaling Wildlife Protection with Autonomous Drones

The conservation of threatened species in South Africa has seen remarkable successes in the past years, but illegal poaching still presents a great danger for a range of animals living in the reserves. 415 rhinos were killed in 2021 alone. The team of Strategic Protection Of Threatened Species (SPOTS) has been working consistently over the years to put an end to illegal poaching. But the monitoring of such a vast area by a limited number of rangers, day and night, is a huge challenge.  

One way to tackle the problem is using UAVs (Unmanned Aerial Vehicles). SPOTS teamed up with FruitPunch AI to crowdsource a team of AI for Good engineers to develop a poacher detection system and put it onto an autonomous drone. Over a series of AI for Wildlife Challenges, thousands of engineering hours have been invested into developing the AI-powered virtual flying ranger. Meeting the unique operating conditions of south African wildlife reserves, its goal is to detect poachers any time of the day autonomously.

This is an account of the 10-week teamwork of us, 22 engineers who’ve picked up from the outcomes of the first two Challenges with AI for Wildlife 3 Challenge and developed multiple software and hardware pipelines improving the efficiency and autonomy of the UAV.

TinyML Flying over South African Savanna

Our AI for Wildlife team was divided up to address 4 subgoals: 

  1. Model Optimization: Improve the performance and inference time of the poacher-detecting computer vision model. 
  1. CI/CD: Set up continuous integration of new training data into the operating model. 
  1. Hardware: Make sure the detection model can run on the drone and secure an orderly data transfer to the ground control station. 
  1. Autonomous Flight: Come up with a safe way to land the plane autonomously. 

With the subteams formed, each of the 4 groups set out to define a more detailed framework of subgoals to work towards over the course of the 10-week challenge. Due to overlap in technology and methods needed to reach the subgoals, the Model Optimization and the CI/CD team worked more closely together, as did the Hardware and the Autonomous Flight team. 

The edge hardware on the autonomous UAV of SPOTS

Of Rhinos and of the Difficult Birth of Production-Ready AI

The protection of endangered species by the SPOT UAV already in use has become more efficient thanks to the results of this challenge. The machine learning algorithms that spot the poachers have become more accurate, faster and more automated. Due to higher efficiency, more poachers can be reprimanded easily. This will lead to better conservation of endangered species and local ecosystems where these species reside. 

We made progress on all fronts but the components are not quite ready for deployment in their edge computing entirety. Automated flight still needs to be moved to the drone in a way that does not collide with object detection. Both functions strain the GPU resources, so they can’t run at the same time yet. More engineering hours will be needed to make all parts of the solution work together. 

Who would’ve thought that bringing TinyML to the edge on an autonomous drone in remote areas of African savanna can be a bit of a challenge ;) 

But we have a mission. We did it … and we will be doing it for the rhino and the communities to which these beloved animals are so important. The rhino is a symbol on the South African banknotes. It is a totem to the Lango community in Uganda. And being part of the big five animals in Africa, the rhino contributes immensely towards the tourism sector. Moreover, it’s a global symbol of the importance of preserving wildlife on Earth for generations to come. 

Team AI for Wildlife 3

Model Optimization: Sahil Chachra, Manu Chauhan, Jaka Cikač, Sabelo Makhanya, Sinan Robillard

CI/ CD: Aisha Kala, Mayur Ranchod, Sabelo Makhanya, Rowanne Trapmann, Ethan Kraus, Barbra Apilli, Samantha Biegel, Adrian Azoitei

Hardware: Kamalen Reddy, Estine Clasen, Maks Kulicki, Thembinkosi Malefo, Michael Bernhardt

Autonomous Flight: Thanasis Trantas, Emile Dhifallah, Nima Negarandeh, Gerson Foks, Ryan Wolf

Read the full version of the AI for Wildlife 3 Case Study

Discover how the team tackled model optimization and pruning, MLOps, deployment on edge hardware of the drone and autonomous landing. Team by team, we’ve documented how the solutions formed and what our teams experienced while building these solutions.

Read the full applied AI case study
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