TY  - JOUR
VL  - 21
IS  - 12
JF  - Trends in Plant Science
Y1  - 2016/10//
SP  - 989
PB  - Elsevier
A1  - Tsaftaris, Sotirios A.
A1  - Minervini, Massimo
A1  - Scharr, Hanno
EP  - 991
ID  - eprints3606
N1  - Available online 31 October 2016
UR  - http://doi.org/10.1016/j.tplants.2016.10.002
KW  - image processing
KW  -  machine learning
KW  -  plant phenotyping
KW  -  stress
AV  - none
TI  - Machine Learning for Plant Phenotyping Needs Image Processing
N2  - 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].
SN  - 1360-1385
ER  -