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Timothy Brown

0000-0002-4735-9526

Lead for Digital Innovation, Australian Plant Phenomics Facility, Australian National University (Biological sciences; Earth sciences; Environmental sciences; Engineering; Plant biology not elsewhere classified; Ecology not elsewhere classified; Carbon sequestration science; Climatology; Ecosystem function; Ecological impacts of climate change and ecological adaptation; Environmental biotechnology diagnostics (incl. biosensors); Environmental biotechnology not elsewhere classified; Communications engineering not elsewhere classified)

Canberra, Australia

Tim Brown is the Lead for Digital Innovation for the Australian Plant Phenomics Facility (APPF), based out of the APPF node at the Australian National University (ANU) in Canberra, Australia. He has a PhD from the University of Utah, USA (2006), in complexity theory and modelling of self-organized swarming behaviour in New World army ants. Tim’s current work focuses on developing open-source hardware and software pipelines for enabling high throughput phenotyping, high resolution monitoring and improving management and visualization of complex research and environmental data. Tim is currently a PI on the ARDC funded Australian Scalable Drone Cloud (ASDC) project to create an open-source national platform for drone data in Australia. In 2014 he led the development of EcoVR, one of the first digital twin projects to use game engine software to model field research sites and visualise time-series environmental data in VR.

Publications

  • https://scholar.google.com/citations?user=nUQw2aIAAAAJ
  • Regional scale impacts of Tamarix leaf beetles (Diorhabda carinulata) on the water availability of western U.S. rivers as determined by multi-scale remote sensing methods
  • High-resolution, time-lapse imaging for ecosystem-scale phenotyping in the field
  • Variation in Leaf Respiration Rates at Night Correlates with Carbohydrate and Amino Acid Supply
  • Monitoring impacts of Tamarix leaf beetles (Diorhabda elongata) on the leaf phenology and water use of Tamarix spp. using ground and remote sensing methods
  • WK 4-1: The landscape of climate change
  • TraitCapture: genomic and environment modelling of plant phenomic data
  • Regional scale impacts of Tamarix leaf beetles (Diorhabda carinulata) on the water availability of western US rivers as determined by multi-scale remote sensing methods
  • Modeling Behavioral Rules and Self-organization in New World Army Ant Swarms
  • Using phenocams to monitor our changing Earth: toward a global phenocam network
  • Using Phenomic Analysis of Photosynthetic Function for Abiotic Stress Response Gene Discovery
  • Interaction rules, information and foraging--a two-dimensional lattice model of self-organized swarming behavior in the army ant Eciton burchelli.
  • Using Webcam Technology for Measuring and Scaling Phenology of Tamarisk (Tamarix ramosissima) Infested with the Biocontrol Beetle (Diorhabda carinulata) on the Dolores River, Utah
  • Phenocamera: automated phenology monitoring
  • Rapid dispersal of saltcedar (Tamarix spp.) biocontrol beetles (Diorhabda carinulata) on a desert river detected by phenocams, MODIS imagery and ground observations
  • Gigavision-A weatherproof, multibillion pixel resolution time-lapse camera system for recording and tracking phenology in every plant in a landscape
  • Using simple rules to solve complex problems--Measurements and modeling of army ant swarms
  • Deep Phenotyping: Deep Learning For Temporal Phenotype/Genotype Classification
  • Assimilating phenology datasets automatically across ICOS ecosystem stations
  • Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe’s terrestrial ecosystems: a review
  • Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography
  • Biology and Modeling of Self-organization in the New World Army Ant Eciton Burchellii
  • Dissertation, Ph. D., The University of Utah, Salt Lake City, UT (USA). Biology and modeling of self-organization in the New World army ant Eciton burchellii.
  • 1. Brown, T. B. & Adler, F. Biology and Modeling of Self-organization in the New World Army Ant Exciton burchellii
  • Deep phenotyping: deep learning for temporal phenotype/genotype classification
  • Rapid dispersal of saltcedar (Tamarix spp.) biocontrol beetles (Diorhabda carinulata) on a desert river detected by phenocams, MODIS imagery and ground observations
  • Using phenocams to monitor our changing earth: Toward a global phenocam network
  • TraitCapture: Genomic and environment modelling of plant phenomic data
  • High-resolution, time-lapse imaging for ecosystem-scale phenotyping in the field
  • Arabidopsis Thermo-tolerance Growth trial - Phenotyping imaging data
  • Gigavision Canberra National Arboretum
  • Towards long-Term standardised carbon and greenhouse gas observations for monitoring Europe's terrestrial ecosystems: A review
  • Reviews and syntheses: Australian vegetation phenology: New insights from satellite remote sensing and digital repeat photography
  • Arabidopsis TR0125 - Phenotyping imaging data
  • Daintree Rainforest, Cow Bay Phenocam Images and Phenology Data Collection
  • Arabidopsis images for machine learning TR0107 - Phenotyping imaging data
  • Brachypodium 240 Accession GWAS (BVZ0077) - Phenotyping imaging data
  • Arabidopsis TR0126 - Phenotyping imaging data
  • Brachypodium 240 Accession GWAS (BVZ0072) - Phenotyping imaging data
  • Arabidopsis TR0036 - Phenotyping imaging data
  • Arabidopsis TR0043 - Phenotyping imaging data
  • Brachypodium 240 Accession GWAS (BVZ0073) - Phenotyping imaging data
  • Gigavision Kioloa Coastal Campus ANU
  • Deep phenotyping: Deep learning for temporal phenotype/genotype classification
  • Variation in leaf respiration rates at night correlates with carbohydrate and amino acid supply
  • Phenocamera: automated phenology monitoring protocol

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Co-workers & collaborators

Adam Steer

Adam Steer

Timothy Brown's public data