Applied Deep Learning in Agri-Food Sector
To overcome complexities of the handled agri-food products, environment, and the difficulties of the manual tasks, the agri-food sector is widely adopting Deep Learning (DL) techniques. It is used for tasks like non-destructive quality assessment of food products, automatic sorting and grading of foods, phenotyping and predicting yield outcomes. At WUR agrofood robotics team, we work on developing solutions for areas of agri, food and life sciences. This talk will shed a light on different use cases we work, such as, produce classification with transfer learning, learning from demonstration and aiding marine life biodiversity monitoring with DL.
Uldanay Bairam is a Computer Vision Research Scientist at Wageningen Food & Biobased Research (WFBR). Uldanay obtained her master's degree with the Erasmus+ International program Colour in Science and Industry with a focus on Image Processing and Data Analysis. Prior to WFBR, she worked as a Teacher Lecturer in IT and did an internship with a plant breeding and seed company Vilmorin on application of deep learning for the phenotyping of plants. Her research is now focused on developing solutions for different areas in agro-food robotics.