Automatic image recognition to determine morphological development and secondary metabolite accumulation in hairy root networks

This study focuses on the morphological development and secondary metabolite production of the red pigments from the group of betacyanins in hairy roots of Beta vulgaris. We demonstrate a working, medium throughput, customized, automatic image recognition solution for hairy roots on agar plates including the evaluation of 12 experimental samples. Image acquisition is conducted under comparable para‐meters using a tripod with light emitting diode background lighting and a digital single lens reflex camera. The server‐based image recognition system developed together with Wimasis GmbH, Munich, Germany helps to obtain not only quantitative values for morphological parameters, such as segment lengths and widths or metabolite concentrations, but also global parameters of root growth, such as total root length or the number of branching points. Using timed diagrams the development of the total root length, the total number of branching points, and the mean pigment concentration during the cultivation period were determined. The generated data present the basis for detailed mathematical modeling in order to achieve a structured growth model for hairy roots. A mathematical model for growth of hairy roots can be used to decrease experimental efforts as well as to optimize cultivation conditions and the bioreactor design.