Automated quantification of fine root production from minirhizotron image time series

Abstract Plant root growth accounts for a major part of the net primary production in grassland and forest ecosystems and influences the global carbon and nutrient cycles. Measuring the production of roots is inherently difficult, prone to inconsistencies and time‐consuming. Notably, there are currently no methods yet to automate this task. We have developed GINGER, a new method for automated estimation of the fine root production from a time series of minirhizotron images. It compares pairs of consecutive images with each other, separating new root growth from standing crop. The method was evaluated on four datasets from grassland, drained fen peatland and forest ecosystems. It exhibits performance on a similar level to that of human annotators while substantially reducing the time required for the data analysis. Human annotators showed a significant degree of variability among each other, confirming that the task is subjective and error‐prone. For demonstration, this pipeline was applied on two real‐world image datasets, spanning 2 and 3 years, to compute the total annual root production. End‐to‐end, including annotation and model training, GINGER reduced the required human workload from several thousand to less than 40 work hours. It could allow to scale up monitoring efforts and enable full automation in the future.