In this article a new method is introduced for distinguishing roots and background based on their digital curvelet transform in minirhizotron images. In the proposed method, the nonlinear mapping is applied on sub-band curvelet components followed by boundary detection using energy optimization concept. The curvelet transform has the excellent capability in detecting roots with different orient...

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 ...

Even though fine-root turnover is a highly studied topic, it is often poorly understood as a result of uncertainties inherent in its sampling, e.g., quantifying spatial and temporal variability. While many methods exist to quantify fine-root turnover, use of minirhizotrons has increased over the last two decades, making sensor errors another source of uncertainty. Currently, no standardized met...

An approach to automate the procedure of extracting and measuring roots in minirhizotron images is presented. By the use of two-dimensional matched filtering and local entropy thresholding, one can efficiently enhance the local contrast of the root and then extract it from the minirhizotron image. We also present several techniques for discriminating roots against extraneous objects based on th...

Minirhizotrons provide a nondestructive, in situ method for directly viewing and studying fine roots. Although many insights into fine roots have been gained using minirhizotrons, a review of the literature indicates a wide variation in how minirhizotrons and minirhizotron data are used. Tube installation is critical, and steps must be taken to insure good soil/tube contact without compacting t...

In this paper a new method is introduced for root detection in minirhizotron images for root investigation. In this method firstly a hypothesis testing framework is defined to separate roots from background and noise. Then the correct roots are extracted by using an entropy-based geometric level set decision function. Performance of the proposed method is evaluated on real captured images in tw...

We compared growth patterns and morphology of fine roots of size-controlling and invigorating peach (Prunus persica (L.) Batsch) rootstocks. Peach trees were grafted on five rootstocks: a vigorous control (Nemaguard), three intermediate vigor rootstocks (K119-50, P30-135 and Hiawatha), and a semi-dwarfing rootstock (K146-43). Minirhizotron tubes were installed at the base of trees on each roots...

This work proposes a piecewise linear network model to approximate structures observed in an image. An energy function is used to capture the characteristics of the structure. The energy function consists of two parts: the prior energy term and the data energy term. The prior energy term is calculated using prior information about the structures of interest. The data energy term is calculated u...