… Author links open overlay panel Jonathan A Atkinson 1 , Michael P Pound 1 2 , Malcolm
J Bennett 1 , … Griffiths, MP Pound, O. Gaju, MJ Foulkes, JL Gouis, S. Griffiths, MJ Bennett, J. …

In plant phenotyping, it has become important to be able to measure many features on large
image sets in order to aid genetic discovery. The size of the datasets, now often captured …

We present a novel image analysis tool that allows the semiautomated quantification of
complex root system architectures in a range of plant species grown and imaged in a variety of …

There is a particular phenotyping demand to accurately quantify images of crops, and the
natural variability and structure of these plants presents unique difficulties. Recently, machine …

… Michael P Pound Michael P Pound … Our pipeline is driven by a deep encoder-decoder
network, similar to that presented by Pound et al. [5] but adapted to handle higher-resolution …

Supervised machine learning techniques are increasingly being combined with ultrasonic
sensor measurements owing to their strong performance. These techniques also offer …

Auxin is a key regulator of plant growth and development. Within the root tip, auxin distribution
plays a crucial role specifying developmental zones and coordinating tropic responses. …

Seedling root traits of wheat (Triticum aestivum L.) have been shown to be important for
efficient establishment and linked to mature plant traits such as height and yield. A root …

Background Convolvulus sepium (hedge bindweed) detection in sugar beet fields remains a
challenging problem due to variation in appearance of plants, illumination changes, foliage …

We address the complex problem of reliably segmenting root structure from soil in X-ray
Computed Tomography (CT) images. We utilise a deep learning approach, and propose a state-…