Abstract
There is an increasing interest in the cultivation of seaweeds for food and feed, and the seaweed aquaculture industry is rapidly developing. The nutritional status of the seaweeds is important to ensure a good quality crop. Cost-efficient and straightforward methods for farmers to analyze their crop are essential for the successful development of the industry. In this study, we developed non-destructive, labor- and cost-efficient models to estimate the nitrogen content in the crop seaweed Ulva fenestrata by color image analysis. We quantified tissue nitrogen content and thallus color in sea-farmed seaweed every week throughout a whole cultivation season (15 consecutive weeks) and analyzed data with linear regression models. We showed that color image analysis accurately estimated the nitrogen content in the seaweed (R 2 = 0.944 and 0.827 for fresh tissue and dried powder, respectively), and through tenfold cross validation we showed that the developed models were robust and precise. Based on these models, we developed a web-based application that automatically analyzes the nitrogen content of U. fenestrata . Furthermore, we produced a color guide that can easily be brought to the farm for onsite crude estimation of the nitrogen content of U. fenestrata . Our results demonstrate that color can be a powerful tool for seaweed farmers (and researchers) to estimate seaweeds’ nutritional status. We anticipate that similar models can be developed for other commercially interesting seaweed species.