Deep learning technologies for giant hogweed eradication
Who is this presentation for?IT engineers and managers who want to know actual use cases and leverage machine learning technologies with Big Data technologies
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, 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 view point of enterprises and tips to leverage the above open source software.
Prerequisite knowledgeIt is desirable to have basic knowledge of Hadoop, Spark and TensorFlow.
What you'll learn
NTT DATA Corporation
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.
NTT Data Corp.
Masaru is a senior IT infrastructure engineer / IT architect and manager of NTT DATA Corporation. He is responsible for the research and development about data processing and analytics platform leveraging open sources (e.g. Hadoop, Spark, Kafka, etc) The clusters which he designed and developed includes thousands of nodes. He had several presentations at Strata Data Conference, Kafka Summit, Spark Summit, DataWorks Summit and so on.
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