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Thursday, December 3 • 13:10 - 14:00
Lessons Learned from the Collaboration of Big Data and AI/ML Technologies for Giant Hogweed Eradication - Naoto Umemori & Masaru Dobashi, NTT DATA
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Giant Hogweed is a highly toxic plant originating in the Western Caucasus. It has spread across Central and Western Europe and there are sightings of Giant Hogweed reported from North America, too. Landowners are obliged to eradicate it, due to its toxicity and invasive nature in Europe. However, it is difficult for landowners to find and remove Giant Hogweed across large areas of land since it is a very cumbersome manual process. To automate the process of detecting the Giant Hogweed by exploiting technologies like drones and image recognition/detection using Machine Learning is an effective way to address this problem. In this presentation, we show you how we designed the architecture towards the Petabyte scale, how we took advantage of both of Big Data and Machine / Deep Learning technologies and lessons learned through this project. For example, we integrated a drone, Apache Hadoop, Apache Spark and TensorFlow to achieve the usability, flexibility and scalability for both of data engineers and data analysts. We talk about why this integration was needed for us, technical challenges from the viewpoint of enterprises and tips to leverage the above open source software.

avatar for Masaru Dobashi

Masaru Dobashi

Executive IT Specialist, Manager, NTT DATA
Masaru Dobashi is a system infrastructure architect and expert on distributed computing, machine learning platform, and stream data processing. He leads the open-source professional service team at NTT DATA Corporation and has responsibility for introducing open source-based data... Read More →
avatar for Naoto Umemori

Naoto Umemori

Deputy Manager, Senior IT Specialist, NTT DATA
Naoto is a Senior IT Specialist and Deputy Manager at NTT DATA Corporation, working on technology and innovation area. He has spent around a decade in the Platform and Infrastructure field, focusing mainly on the open source software technology stack. He has experiences with talking... Read More →

Thursday December 3, 2020 13:10 - 14:00 JST
Virtual 3