Analysis of root images from auger sampling with a fast procedure: a case of application to sugar beet

Abstract

Manual line-intersect methods for estimating root length are being progressively replaced by faster and more accurate image analysis procedures. These methods even allow the estimation of some more root parameters (e.g., diameter), but still require preliminary labour-intensive operations. Through a task-specific macro function written in a general-purpose image analysis programme (KS 300 – Zeiss), the processing time of root images was greatly reduced with respect to skeletonisation methods by using a high-precision algorithm (Fibrelength). This has been previously proposed by other authors, and estimates length as a function of perimeter and area of the digital image of roots. One-bit binary images were acquired, aiming at large savings in computer memory, and automatic discrimination of roots against extraneous objects based on their elongation index (perimeter²/area), was performed successfully. Of four tested spatial resolutions (2.9, 5.9, 8.8, 11.8 pixel mm⁻¹), in clean samples good accuracy in root length estimation was achieved at 11.8 pixel mm⁻¹, up to a root density of 5 cm cm⁻² on the scanner bed. This resolution is theoretically suitable for representing roots at least 85 μm wide. When dealing with uncleaned samples, a thick layer of water was useful in speeding up spreading of roots on the scanner bed and avoiding underestimation of their length due to overlaps with organic debris. A set of fibrous root samples of sugar beet (Beta vulgaris var. saccharifera L.) collected at harvest over two years at Legnaro (NE Italy) was analysed by applying the above procedure. Fertilisation with 100 kg ha⁻¹ of nitrogen led to higher RLD (root length density in soil) in shallow layers with respect to unfertilised controls, whereas thicker roots were found deeper than 80 cm of soil without nitrogen.