Wei Guo

Wei Guo

University of Tokyo

Wei Guo is currently working as an Assistant professor at The University of Tokyo, Japan. He established “International Field Phenomics Research Laboratory”, the first plant phenomics laboratory in Japan, in 2017 as a core member. His research focuses on field-based phenotyping using advanced sensing platforms and technologies such as drones and ground robots, image processing, and machine learning approaches.

Deep learning based plant phenotyping with limited labels

Deep neural networks have shown impressive performance enhancements on plant phenotyping tasks, such as organ detection, disease identification, etc. However, these achievements are often difficult to translate into real-world applications because they usually require an extensive amount of manually labeled training datasets. Preparing such training datasets for plant phenotyping is labor-intensive and time-consuming due to the single category, high-density objects within one picture, and requires a sufficient amount of domain knowledge. Moreover, most of the training datasets are prepared especially for a specific task in a particular domain, so they must be relabeled when the task or domain changes. In this presentation, I will introduce several approaches that we have developed/will develop to overcome the limited labeled training dataset issues.