Owing to the rapid technical advances in the field of computer vision and machine learning, the complexity of information that can be processed automatically is nearly reaching that of human level. Particularly in the plant-science/agriculture domain, incorporation of such technique (especially deep learning related matters) is expected to greatly enhance the efficiency of quantitative/qualitative plant phenotyping when using image data. In this seminar, I would like to introduce arrays of ongoing plant phenotyping projects (crop disease diagnosis and detection, stomata aperture quantification, leaf segmentation and counting, seed quantification etc.) which should motivate ourselves enough to be optimistic to “re-challenge” image based plant phenotyping. Domain specific bars and challenges will also be discussed, involving several solution proposals. Moreover, I would like to share the recent plant phenotyping platform projects that I have started in the company recently founded, demonstrating the possibility to extend outside the academic domain.