Deep learning technologies for giant hogweed eradication

Description

Giant hogweed is a highly toxic plant originating in the Western Caucasus. It spread across central and western Europe, and there are sightings of giant hogweed reported from North America. Landowners are obliged to eradicate it due to its toxicity and invasive nature in Europe. However, it’s difficult for landowners to find and remove giant hogweed across large areas of land, since it’s a very cumbersome manual process.

Automating the process of detecting giant hogweed by exploiting technologies like drones and image recognition and detection using machine learning is an effective way to address this problem. Naoto Umemori and Masaru Dobashi outline how they designed the architecture, took advantage of big data and machine and deep learning technologies, and the lessons learned through this project. For example, they integrated a drone, Apache Hadoop, Apache Spark, and TensorFlow to achieve the usability, flexibility, and scalability for data engineers and data analysts. You’ll discover why this integration was needed, technical challenges from the viewpoint of enterprises, and tips to leverage this open source software.