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AI Use Cases

A collection of over 250 uses for artificial intelligence

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Endangered species detection and tracking

Introduction

This paper has the aim of explaining why and how the protection of threatened species is crucial given the present rate of loss of the biological diversity around the globe. The conventional techniques used in the monitoring of wildlife have been known to be cumbersome, tedious, and at times, inconclusive. Machine Learning (ML) and Computer Vision (CV) are the two major branches of Artificial Intelligence (AI), which have the potential to change the way wildlife conservation is done by enhancing the detection and tracking of endangered species. This technology applies algorithms to information sets to identify trends and later on, to forecast, hence providing a more efficient and effective way of monitoring wildlife quantities.

Challenges

There are however some challenges that come with the integration of this technology in wildlife conservation. The first challenge is that the areas where the endangered species are found are vast and complex which makes data collection difficult. Other factors that may affect the quality of data include; severe weather conditions, excessive vegetation and the behavior of some organisms. Second, the expense of AI technology, including the expenses on hardware and software, can be beyond the reach of many conservation organizations. Third, the lack of knowledge and skills on AI among the conservationists is another challenge that may affect the integration of this technology. Finally, there are ethical concerns on the matter of invasion of wildlife privacy as well as the proper use of data collected.

AI Solutions

There are however several challenges that one is likely to encounter in the process of using AI in the detection and tracking of endangered species. ML algorithms can be applied to the analysis of camera trap photos and this makes it easier and faster to detect different species than when one has to do it manually. Other subfields of machine learning including deep learning can also be applied in the identification of specific animals by their appearance for instance their face or stripes. CV algorithms can use satellite and drone data to monitor changes in habitat and the location of large numbers of animals. In addition, AI can be applied to the future status of the species and the assessment of risks to their existence.

Benefits

There are numerous advantages of applying AI in the detection and tracking of endangered species. This is because AI can help in the analysis of data thus enabling better and quicker decisions to be made in the conservation efforts. It also minimises the costs of monitoring by making the process of species identification and tracking automated. Such predictions of future trends of the population can assist the conservationists to be proactive in their protection of the species. In addition, AI can help in understanding the behavior and habitat of the wildlife and therefore provide a better insight of the species and the conditions under which they live.

Return on Investment

It is important to note that while the deployment of AI technology may require a significant capital investment at the outset, the benefits of AI are not only felt in the short run but can be quite substantial in the long run as well. In this way, AI has the potential to reduce the time and money spent on wildlife monitoring which can then be used for other conservation activities. In addition, through the enhancement of data quality and coverage, AI can enable the creation of better conservation plans thus possibly decreasing the costs of conservation interventions in the future.

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