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A collection of over 250 uses for artificial intelligence

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Species counting

Introduction

The use of Machine Learning (ML), Computer Vision (CV) and Artificial Intelligence (AI) in the wildlife management is a relatively new and promising area that has the capacity of bringing about a change in species counting and conservation of wildlife. With the ability to process large data sets with a high level of accuracy these technologies have the potential of changing the way we view and address issues concerning biodiversity. From identifying and classifying species in the wild to assessing their future numbers, ML, CV and AI are being used to help mitigate the problems faced by wildlife conservation.

Challenges

It is important to note that species counting in the wildlife industry is not an easy task and is plagued with numerous challenges. The traditional methods of species counting such as the field surveys are strenuous, slow, and sometimes associated with high costs and inaccuracies due to the involvement of humans. However, there are other barriers that include; the challenge of observing animals in the presence of other factors such as trees and buildings, the unwillingness of the animals, and the large areas that need to be covered. Furthermore, there are problems that include confusion between species that look alike or young animals and the problem of counting the same animal more than once. Also, in most cases, these surveys can interfere with the normal activities of the animals.

AI Solutions

There are several challenges that affect the accurate and timely detection of animals and these are; AI, ML and CV provide smart solutions to these problems. These technologies can therefore use images or videos that are taken by drones, camera traps or satellites to identify and or count animals. The algorithms can be developed in a way that they are able to identify certain species of animals and distinguish between the two in their natural environment. This technology can also be applied in the tracking of animal movements which is very useful in the analysis of population dynamics. For instance, the Microsoft AI for Earth initiative offers grants, cloud computing and other AI tools for initiatives that seek to shift how the world monitors, models, and manages planet’s biodiversity. Also, Google’s AI for Social Good program has a ‘Wildlife Insights’ initiative that applies machine learning to images from camera traps to monitor animals in the wild.

Benefits

There are a number of advantages of employing ML, CV and AI in species counting. These technologies enhance the efficiency and precision of species counting as well as minimize time, funds and effort. They enable real time or near real time observation of wildlife which enables fast interventions. In addition, these technologies can help in analyzing the long term patterns and make forecasts on future shifts in wildlife population. This can assist in the formulation of conservation measures and policies and also assist in determining the areas that require conservation attention. Also, AI can be applied in involving people in the conservation processes since it makes the data easy to understand.

Return on Investment

It is possible to realize huge returns on investment (ROI) when using ML, CV, and AI in species counting. Although there is a lot of investment required to set up these technologies, the return on investment in terms of reduced manpower and enhanced efficiency in the long run can be quite high. In addition, the enhanced value of these technologies is not only quantifiable. The enhancement in the accuracy and the speed of species counting can enhance the efficiency of conservation measures that in turn has positive impacts on the biodiversity and the environment. This is because AI can also be used to prevent costly conservation mistakes such as early warnings on population declines or shifts through prediction.

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