eprintid: 3606
rev_number: 5
eprint_status: archive
userid: 6
dir: disk0/00/00/36/06
datestamp: 2016-11-30 10:37:34
lastmod: 2016-11-30 10:37:34
status_changed: 2016-11-30 10:37:34
type: article
metadata_visibility: show
creators_name: Tsaftaris, Sotirios A.
creators_name: Minervini, Massimo
creators_name: Scharr, Hanno
creators_id: 
creators_id: massimo.minervini@imtlucca.it
creators_id: 
title: Machine Learning for Plant Phenotyping Needs Image Processing
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
keywords: image processing, machine learning, plant phenotyping, stress
note: Available online 31 October 2016
abstract: We found the article by Singh et al. [1] extremely interesting because it introduces and showcases the utility of machine learning for high-throughput data-driven plant phenotyping. With this letter we aim to emphasize the role that image analysis and processing have in the phenotyping pipeline beyond what is suggested in [1], both in analyzing phenotyping data (e.g., to measure growth) and when providing effective feature extraction to be used by machine learning. Key recent reviews have shown that it is image analysis itself (what the authors of [1] consider as part of pre-processing) that has brought a renaissance in phenotyping [2].
date: 2016-10
publication: Trends in Plant Science
volume: 21
number: 12
publisher: Elsevier
pagerange: 989-991
id_number: doi:10.1016/j.tplants.2016.10.002
refereed: TRUE
issn: 1360-1385
official_url: http://doi.org/10.1016/j.tplants.2016.10.002
citation:   Tsaftaris, Sotirios A. and Minervini, Massimo and Scharr, Hanno  Machine Learning for Plant Phenotyping Needs Image Processing.  Trends in Plant Science, 21 (12).  pp. 989-991.  ISSN 1360-1385      (2016)