Justin Borevitz

Key Recent Papers

JG Bragg, MA Supple, RL Andrew, JO Borevitz Genomic variation across landscapes: insights and applications New Phytologist (2015)

Grabowski PP, Morris GP, Casler MD, Borevitz JO. Population genomic variation reveals roles of history, adaptation and ploidy in switchgrass. Mol Ecol. 2014 Jun  (online)

Brown TB, Cheng R, Sirault XR, Rungrat T, Murray KD, Trtilek M, Furbank RT, Badger M, Pogson BJ, Borevitz JO. TraitCapture: genomic and environment modelling of plant phenomic data. Curr Opin Plant Biol. 2014 Apr (online)

Li Y, Cheng R, Spokas KA, Palmer AA, Borevitz JO. Genetic Variation for Life History Sensitivity to Seasonal Warming in Arabidopsis thaliana. Genetics. Feb 2014 (online)

Xu Zhang, Ron Hauss, Justin Borevitz. Natural Genetic Variation for Growth and Development Revealed by High-Throughput Phenotyping in Arabidopsis thaliana (G3 genetics Jan 2012) (Online)

Benjamin Brachi, Geoff Morris, Justin Borevitz. Genome Wide Association Studies in Plants: The missing heritability is in the field. Genome Biology, Oct 28, 2011. (online)

Li Y, Huang Y, Bergelson J, Nordborg M, Borevitz JO. Association Mapping of Local Climate Sensitive QTL in Arabidopsis thaliana. PNAS, Nov 15, 2010. (Online)


 

Full Pubmed Listing

pubmed: borevitz,justin[auth...

NCBI: db=pubmed; Term=borevitz,justin[Author - Full]

Related Articles

Deep phenotyping: deep learning for temporal phenotype/genotype classification.

Plant Methods. 2018;14:66

Authors: Taghavi Namin S, Esmaeilzadeh M, Najafi M, Brown TB, Borevitz JO

Abstract
Background: High resolution and high throughput genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. More recently, CNNs have been used for plant classification and phenotyping, using individual static images of the plants. On the other hand, dynamic behavior of the plants as well as their growth has been an important phenotype for plant biologists, and this motivated us to study the potential of LSTMs in encoding these temporal information for the accession classification task, which is useful in automation of plant production and care.
Methods: In this paper, we propose a CNN-LSTM framework for plant classification of various genotypes. Here, we exploit the power of deep CNNs for automatic joint feature and classifier learning, compared to using hand-crafted features. In addition, we leverage the potential of LSTMs to study the growth of the plants and their dynamic behaviors as important discriminative phenotypes for accession classification. Moreover, we collected a dataset of time-series image sequences of four accessions of Arabidopsis, captured in similar imaging conditions, which could be used as a standard benchmark by researchers in the field. We made this dataset publicly available.
Conclusion: The results provide evidence of the benefits of our accession classification approach over using traditional hand-crafted image analysis features and other accession classification frameworks. We also demonstrate that utilizing temporal information using LSTMs can further improve the performance of the system. The proposed framework can be used in other applications such as in plant classification given the environment conditions or in distinguishing diseased plants from healthy ones.

PMID: 30087695 [PubMed]