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.