Automatic discrimination of fine roots in minirhizotron images
2008
Zeng, Guang | Birchfield, Stanley T. | Wells, Christina E.
• Minirhizotrons provide detailed information on the production, life history and mortality of fine roots. However, manual processing of minirhizotron images is time‐consuming, limiting the number and size of experiments that can reasonably be analysed. Previously, an algorithm was developed to automatically detect and measure individual roots in minirhizotron images. Here, species‐specific root classifiers were developed to discriminate detected roots from bright background artifacts. • Classifiers were developed from training images of peach (Prunus persica), freeman maple (Acer × freemanii) and sweetbay magnolia (Magnolia virginiana) using the Adaboost algorithm. True‐ and false‐positive rates for classifiers were estimated using receiver operating characteristic curves. • Classifiers gave true positive rates of 89–94% and false positive rates of 3–7% when applied to nontraining images of the species for which they were developed. The application of a classifier trained on one species to images from another species resulted in little or no reduction in accuracy. • These results suggest that a single root classifier can be used to distinguish roots from background objects across multiple minirhizotron experiments. By incorporating root detection and discrimination algorithms into an open‐source minirhizotron image analysis application, many analysis tasks that are currently performed by hand can be automated.
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