A high-throughput method to quantify root hair area in digital images taken in situ

Abstract

Background and aims: Root hair growth and development are important features of plant response to varying soil conditions and of nutrient and water uptake. Most current methods of characterizing root hairs in the field are unreliable or inefficient. We describe a method to quantify root hair area in digital images, such as those collected in situ by minirhizotron systems. Methods: This method uses ImageJ and R open source software and is partially automated using code presented here. It requires manual tracing of a subset of root hair images (training data set) to which a multivariate logistic regression is fit with each color channel in the image as an independent variable. Thereafter the model is applied to complete sets of selected root hair sections to estimate total root hair area. Results: There was good agreement between the training data sets and the predictions of the regression models in castor (Ricinus communis L.), maize (Zea mays L.), and papaya (Carica papaya L.). Conclusion: This method enables time-efficient and consistent quantification of root hairs using in situ root imaging systems that are already widely in use.