Campus Units

Mechanical Engineering, Agronomy, Electrical and Computer Engineering

Document Type

Article

Publication Version

Published Version

Publication Date

2-2016

Journal or Book Title

Trends in Plant Science

Volume

21

Issue

2

First Page

110

Last Page

124

DOI

10.1016/j.tplants.2015.10.015

Abstract

Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.

Comments

This article is published as Singh, Arti, Baskar Ganapathysubramanian, Asheesh Kumar Singh, and Soumik Sarkar. "Machine learning for high-throughput stress phenotyping in plants." Trends in plant science 21, no. 2 (2016): 110-124. DOI:10.1016/j.tplants.2015.10.015. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf