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)
Full text not available from this repository.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].
| Item Type: | Article |
|---|---|
| Identification Number: | https://doi.org/10.1016/j.tplants.2016.10.002 |
| Additional Information: | Available online 31 October 2016 |
| Uncontrolled Keywords: | image processing, machine learning, plant phenotyping, stress |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Research Area: | Computer Science and Applications |
| Depositing User: | Ms T. Iannizzi |
| Date Deposited: | 30 Nov 2016 10:37 |
| Last Modified: | 30 Nov 2016 10:37 |
| URI: | http://eprints.imtlucca.it/id/eprint/3606 |
Actions (login required)
 |
Edit Item |
