Earth, Environment and The Plant Ecosystem - Palaxy!

Revathi Kannan (~revathi00)


Description:

Crowd-sourcing though been practiced for quite long but the term evolved hugely in the recent past. And there are no much projects worked in crowd sourcing related to the environment. Environment itself requires more care nowadays and the plant ecosystem is shrinking day-by-day for the increasing human needs. In addition, protection of the environment is also much necessary for the human survival.

Palaxy , a Crowd-sourcing system for Plant Database is an application to maintain the crowd-source database of plants to aid common people to be benefited by understanding the usage of the plants, and thus protect the endangered species by understanding its importance.

The backbone of the application is built on Machine Learning – Computer Vision.

Prerequisites:

Basics of Computer vision subjects such as Object recognition and categorization, Image processing, Feature detection and matching, Feature-based alignment etc.

Content URLs:

Slideshare : Palaxy, Powerpoint presentation : https://www.slideshare.net/secret/7EkPcGZZgxNqwV

Github repo : https://github.com/RevathiKannanGithub/palaxy

Video URL : https://youtu.be/02SivVFYcqU

Speaker Info:

Revathi Kannan is a B.Tech graduate in Information Technology with a long-term interest in Entrepreneurship. She is currently working as a Lead – Software Engineering at Fidelity Investments, India. She has keen interest in exploring projects in Social entrepreneurship.

Speaker Links:

Project captain for BlueDOT(Open source) which is a “drivetrain” approach aimed to improve the traffic discipline, using the lever of emergency vehicle prioritization. It was exhibited at Software Freedom day 2018 in Chennai organized by Free Software Foundation TamilNadu. Below link has elaborate details about it: https://goinggnu.wordpress.com/2018/09/24/software-freedom-day-2018-chennai-minutes/

Contributed to the building of keralarescue.in: https://github.com/IEEEKeralaSection/rescuekerala

Id: 1444
Section: Data Science, Machine Learning and AI
Type: Talks
Target Audience: Intermediate
Last Updated: