NewsSolving the Extinction Crisis with Edge AI

Solving the Extinction Crisis with Edge AI

Category articles

The machine learning (ML) is an innovative technology that has led to the development of the development of new and innovative applications. However in many ways the technology is constrained due to the fact that it is usually restricted to high-performance computers and data centers. AI (AI) can be considered an integral component of ML and taking it to the forefront is challenging the notion of a limit and opening up new uses which were previously unobtainable.

One example of this is the conservation of wildlife. Organisations working in the field of wildlife conservation are using ML inference on the edges to research, track and safeguard endangered species. Tools like environmental sensors and tracking cameras generate information that can be combined with ML to help better inform conservation and protection initiatives.

In this blog we will look at the ways in which Conservation X Labs worked closely with Edge Impulse, the leading platform for development of ML on devices with edge technology in order to create solutions to bring the edge of AI to the conservation of wildlife field.

Conservation X Labs: Machine Learning for Wildlife Conservation

Conservation X Labs aims to come up with solutions that are fueled by technology and innovation to stop six mass extinctions. They are focused on the causes of the problem and not just the symptoms to tackle the problem from the root.

To aid in these efforts, Conservation X Labs engineered innovative tracking and monitoring technologies like Sentinel to assist in stopping the trafficking of wildlife, slow spreading of species that are invasive and help maintain healthy ecosystems. An AI toolkit based on Google Coral, Sentinel connects to devices such as trail cameras to provide advanced AI capabilities. For instance, with Sentinel, Conservation X Labs can convert a standard trail camera into a motion-tracking camera that can leverage ML to perform automated identification and classification of wildlife for conservation research.

When designing these types of systems, one of the most crucial attributes is monitoring in real time. Real-time monitoring means that cameras on trails can monitor or detect live footage or images of animals. The key to success in real-time monitoring is a low-latency algorithm.

Generallyspeaking, ML applications depend in cloud-based computing centers in order to perform ML algorithms that are computationally intensive. For applications such as Conservation X Labs’, cloud computing isn’t an appropriate solution.

Leveraging Edge Computing for Wildlife Conservation

One of the main reasons cloud computing isn’t suitable as a solution for Conservation X Labs’ application is the fact that wildlife tracking and detection devices are usually placed in remote or remote areas. This can make it difficult for monitors to get access to the wireless connectivity with high bandwidth needed to satisfy the requirements of cloud computing. When you are out in the field in the forest, connectivity to cellular networks is almost non-existent.

Additionally, wireless communication may need more energy and security charges. In order for the wildlife protection devices effective in their work they must have the longest battery life they can get since batteries replacements for remote-operated cameras are not a viable alternative. Therefore, the aim is usually to reduce the amount of information sent between the system.

All of these elements all led Conservation Labs X to one conclusion: the tools they deploy require edge computing. By running algorithms at an edge Conservation Labs X designed tools to monitor wildlife in live performance in real-time without the costs and time-consuming overheads associated when using cloud computing.

Edge Computing Problems

Conservation Labs X quickly encountered major challenges when designing edge-AI tools for wildlife conservation.

One of the challenges was the variety of models required to monitor different species of animals since each animal requires its own data set and model training. The amount of cloud resources needed to constantly develop new models was massive and the costs were also excessive, with prices ranging from the hundreds of thousands, which is unsustainable from a business point of view.

Another significant problem was maintaining a constant pace with the evolving and rapid-paced ML field, specifically its latest technology that include advanced algorithms, brand new libraries, and ever-changing dependencies. To ensure that conservation efforts are carried out efficiently, Conservation Labs X applied the most advanced tools available, which is not an easy task given that the current state of the art changes constantly. Staying up to date is not just difficult but it is also unaffordable, since it distracts the developers from the important issues, like the efficiency of the solution.

Role of Edge Impulse Role of Edge Impulse

After having tried a number of alternative tools Conservation Labs X discovered Edge Impulse as an excellent solution to these problems.

Edge Impulse’s technology makes development, optimization, as well as implementation of ML models on the edge extremely simple and accessible. The platform allows developers to manage their workloads at a higher level covering all aspects of data prep to the selection of data to the selection of models modeling, model training, as well as model implementation, which includes devices-specific binaries.

Edge Impulse completely automates these processes , and utilizes the most current libraries and dependencies, ensuring that Edge Impulse’s ML solutions are on the latest technology. The developers are able to remove these more specialized backend tasks.

Finding a solution to the Extinction Crisis

To ensure the protection of biodiversity and nature essential to life on our world, Conservation Labs X needed modern technologies to increase the efficiency and effectiveness that conservation activities. In the present, ML on the edge has created new use cases that have revealed a clear path in addressing the impending threat of extinction.

With the help of Edge Impulse, Conservation Labs X designed edge devices to track, detect and ultimately , safeguard endangered wildlife. Conservation Labs X believes these advanced technologies will assist in restoring the balance of the natural world, and also prevent the occurrence of future crises.

Michal Pukala
Electronics and Telecommunications engineer with Electro-energetics Master degree graduation. Lightning designer experienced engineer. Currently working in IT industry.