In the present work, the development of a Deep Learning (DL) based computer vision system to automate the artichoke seedling quality control is described, as well as its integration into an IIoT and robotic platforms to perform the transplanting procedure according to the results of the computer vision system. First, the software architecture was designed by taking local and cloud servers, communication protocols, and the logic of operation into account. Second, the computer vision system and the local and cloud version of a web-based graphical user interface (GUI) were developed. Third, both the computer vision and the IIoT platform are integrated intro a Cartesian robot designed to handle seedlings arranged in plug trays. Finally, the results obtained in each phase are shown, highlighting the correlation of our proposed integrated system with the quality control classification standard of an industrial nursery of the region.