A Distributed Machine Learning For Giant Hogweed Eradication

Naoto Umemori and Masaru Dobashi, NTT DATA

Abstract: 

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. However, we had to solve issues like below.

  • How to estimate the habitat or geographical information of the Giant Hogweed from 4K size aerial photographs.
  • Data utilization and image learning/inference infrastructure are necessary since the amount of data of the aerial photographs to handle becomes Terabyte class.
  • On the other hand, if dedicated clusters are constructed for each process and used, the operation becomes complicated.

In this session, we will show how to integrate a drone, Apache Hadoop, Apache Spark, and TensorFlow to solve the above with the architecture and we will introduce it while referring to the processing method.

Naoto Umemori, NTT DATA

Naoto is a Senior Infrastructure Engineer and Deputy Manager at NTT DATA Corporation, working on technology and innovation area. He has spent around 10 years in the Platform and Infrastructure field, focusing mainly on the Open Source Software Technology Stack. Masaru is a senior IT infrastructure engineer/IT architect and manager of NTT DATA Corporation. He is responsible for the research and development of the data processing and analytics platform.

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BibTeX
@conference {232957,
author = {Naoto Umemori and Masaru Dobashi},
title = {A Distributed Machine Learning For Giant Hogweed Eradication},
year = {2019},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
}