Computer-based machine learning is becoming a significant part of citizen science projects. If we compared the situation ten years ago, the data scientists did not depend on computers for their researches. Instead, they relied on manual work of volunteers who were ready to present their skill to organize the data sets that the small researching team of this scientist could not go alone.
The University of Minnesota research
When the University of Minnesota conducted a research to find the role of machine learning and computers in data collection, they found that citizen science projects are using computers a lot. And their research was so much accepted that the science ecological society used made it a cover story for their journal.
The University of Minnesota research drew a comparison between the role of citizen scientist in the present and earlier times. Earlier when the ecologist needed their help, these scientists helped them in classifying and processing the images within a given deadline. However, with the advent of trap cameras, things changed considerably. Now, these citizen scientists helped the ecologist in installing trap cameras which can be useful in their ecological studies.
These cameras are not ordinary ones; rather they have motion sensors and infra-red technology that help the ecologists to get the image of animals while they are moving. Now with such images, these citizen scientists can feed the right skills in the computers so that they can identify animals even through few images.
Moreover, the research also assures that with the installing of camera traps, the ecological scientists succeed in collecting countless images. And through the inclusion of computer-based machine learning, the time which the data scientists took earlier to classify images, reduced greatly.
How the computer identify the image and classify them?
The computer was able to recognize a specific image through machine learning techniques. The citizen scientist employs the machine learning techniques so that the computers can classify through the human classifications of image datasets. For example, if we show an image of a zebra taken from different angles to the machine, it will be able to recognize the image on the basis of patterns and different parts of the zebra.
The research conclusive findings
Therefore, through the employment of machine learning techniques, the University of Minnesota research made some conclusive findings. The research concluded that with machine learning techniques, the ecological researchers were able to save a lot of time in the image classification process. And in doing so, they can make their future bright be getting more citizen scientist projects in the future.
Although the focus of the University of Minnesota research was entirely on the use of machine learning techniques on the ecological process, these techniques are also applicable in other scientific fields. Thus, other citizen science projects that focus on space image classification may use these techniques as well.
However, the research does not conclude that machine learning techniques in citizen scientist projects eliminate the element of human effort. In fact, these techniques will streamline human effort and pave the way for faster classification of data.
Note: Information used in this article are from https://www.sciencedaily.com/releases/2019/02/190206115602.htm