Statistics, Bioinformatics, and Machine Learning Methods in Phenomics

Today, modern plant phenotyping applications are challenging our existing methods for statistical and computational analyses. There is a great need for new analytical approaches that can integrate multiple types of data or provide proper experimental design in observational contexts. This need will only grow with the development of imaging, sequencing, and sensing technologies. A recent push in this direction has been an emphasis on machine learning and artificial intelligence. However, to date most AI applications require further development in order to be useful in phenotyping contexts. This may involve tailored algorithms that incorporate physical and biological constraints, or a reduction in the need for vast amounts of training data. Advances in statistics, bioinformatics, and machine learning will enable the plant phenotyping community to address pressing societal challenges such as responsible ecological management and sustainable improvements in crop productivity. These papers present novel analytical methodology and applications including standards of practice, experimental design, software, scientific reviews, and position pieces

Jennifer Clarke, University of Nebraska-Lincoln

James Schnable, University of Nebraska-Lincoln

The Use of High-Throughput Phenotyping for Assessment of Heat Stress-Induced Changes in Arabidopsis

Plant Phenomics, vol. 2020, Article ID 3723916, 14 pages, 2020.

Ge Gao, Mark A. Tester, and Magdalena M. Julkowska

A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis

Plant Phenomics, vol. 2020, Article ID 7481687, 8 pages, 2020.

Ronghao Wang, Yumou Qiu, Yuzhen Zhou, Zhikai Liang, and James C. Schnable

Computing on Phenotypic Descriptions for Candidate Gene Discovery and Crop Improvement

Plant Phenomics, vol. 2020, Article ID 1963251, 4 pages, 2020.

Ian R. Braun, Colleen F. Yanarella, and Carolyn J. Lawrence-Dill

Semantic Segmentation of Sorghum Using Hyperspectral Data Identifies Genetic Associations

Plant Phenomics, vol. 2020, Article ID 4216373, 11 pages, 2020.

Chenyong Miao, Alejandro Pages, Zheng Xu, Eric Rodene, Jinliang Yang, and James C. Schnable

Generalized Linear Model with Elastic Net Regularization and Convolutional Neural Network for Evaluating Aphanomyces Root Rot Severity in Lentil

Plant Phenomics, vol. 2020, Article ID 2393062, 11 pages, 2020.

Afef Marzougui, Yu Ma, Rebecca J. McGee, Lav R. Khot, and Sindhuja Sankaran