Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

ABSTRACT: 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 robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>

SUBMITTER: Pound MP 

PROVIDER: S-EPMC5632296 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature