About

Luke Woodbury is a research software engineer with extensive experience in machine learning, data science, and hardware integration. He is currently working as an R&D Software Engineer at Plenty, Inc., where he is responsible for prototyping and scaling computer vision solutions for indoor agriculture. He is a generalist who spreads across machine learning modeling, big data analysis, building data pipelines, writing user interfaces, integrating camera hardware, and helping his team grow. Luke has automated away over $240k in wages in 2021, managed a hardware/software integration project with a $22k budget and time of two consultants, provided technical assistance and mentorship to developers in his team, and developed computer vision algorithms for plant quality assurance. He also provides big data visualizations and analyses using SQL as requested by teammates. In the past, Luke has worked as a Machine Learning Engineer at Plenty, Inc., where he explored computer vision solutions for indoor agriculture. He developed a system for measuring plants to an accuracy within 1cm, developed a Google API Python wrapper which uploaded over 500GB of data to Google Drive over the course of two years, and developed user interfaces that connected user input, camera hardware, Google Drive, AWS S3, and a Snowflake database. He also created a moveable, deployable, self-calibrating camera array system capable of measuring over 100 plant sites in 10 seconds, eliminating the need for hand measurements of plant canopies. He developed object detection models for plant observation using neural networks in Tensorflow and proved the value of scalable proxy measurements using correlation and principal component analysis. Luke has also worked as a Product Development Intern at Bright Agrotech, where he used distributed systems to collect visual data from vertical farms and store the data in databases. He developed a robotic camera system capable of self-localization using QR codes, and storing well-labeled images. Luke holds a Bachelor of Science (BS) in Computer Science and a Bachelor of Arts - BA in Music from the University of Wyoming. He is proficient in software engineering, computer vision, machine learning, big data analysis, object detection, SQL, integration, AWS, QA, data science, research science, Snowflake, Python, Tensorflow, and neural networks. He has a relevant experience of 5.86 years.