Statistics, Bioinformatics, and Machine Learning Methods in Phenomics

Special Issue


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.

    Guest Editors

    Jennifer Clarke, University of Nebraska-Lincoln

    James Schnable, University of Nebraska-Lincoln

      Table of Contents

      Detection of the Progression of Anthesis in Field-Grown Maize Tassels: A Case Study

      Seyed Vahid Mirnezami, Srikant Srinivasan, Yan Zhou, Patrick S. Schnable, and Baskar Ganapathysubramanian

      Plant Phenomics, Vol. 2021, Article ID 4238701, 14 pages | Mar 3, 2021

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

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

      Plant Phenomics, Vol. 2020, Article ID 2393062, 11 pages | Nov 26, 2020

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

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

      Plant Phenomics, Vol. 2020, Article ID 3723916, 14 pages | Jul 17, 2020

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

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

      Plant Phenomics, Vol. 2020, Article ID 7481687, 8 pages | Jul 14, 2020

      Computing on Phenotypic Descriptions for Candidate Gene Discovery and Crop Improvement

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

      Plant Phenomics, Vol. 2020, Article ID 1963251, 4 pages | May 20, 2020