GRDC Investments

Results found: 131 - 140 of 205 search results

• GRDC Code: UMU2003-004RSX

  GRS - (Rhys Copeland) Determining the spatial distribution of P. quasitereoides/P. curvicauda in the WA wheatbelt, and understanding how…

  This research project is divided into two parts. In the first part, the aim is to address the identity of root-lesion nematode (RLN) populations present in the WA wheatbelt, with a focus on determining whether populations identified as P.…

  Project start date:

  01/03/2020

  Project end date:

  31/08/2023

  Crop type:

  • Wheat, (Cereal)

  Region:

  West

  Project Status: Completed
• GRDC Code: CSP2002-006RTX

  Frost SENSE: An integrated modelling framework to rapidly map the extent of stem and reproductive frost damage in wheat and barley

  Frost risk occurs virtually every year across southern and eastern Australian agricultural regions. Actual occurrence of frost is determined by location and landscape factors as well as climate. This project aims to demonstrate the capability of…

  Project start date:

  28/02/2020

  Project end date:

  30/01/2024

  Crop type:

  • Wheat, (Cereal)
  • Barley, (Cereal)

  Region:

  North, South, West

  Project Status: Completed
• GRDC Code: UOA2002-007RTX

  Machine learning to extract maximum value from soil and crop variability

  The yields of major crops in Australia are often below their water-limited potential. A reason for this is the complexity of Genotype x Environment x Management (GxExM) interaction, which results in crop growth with high variability. This project…

  Project start date:

  24/02/2020

  Project end date:

  30/06/2022

  Crop type:

  • Wheat, (Cereal)
  • Barley, (Cereal)

  Region:

  North, South, West

  Project Status: Completed
• GRDC Code: UOQ2002-010RTX

  CropPhen: Remote mapping of grain crop type and phenology

  This project aims to develop a software-based tool to remotely map crop type (wheat, barley, chickpea, lentils, sorghum and mungbean) and development stage at the sub-paddock scale. The project will combine remote sensing, crop simulation modelling, …

  Project start date:

  24/02/2020

  Project end date:

  30/06/2024

  Crop type:

  • Barley, (Cereal)
  • Sorghum, (Cereal)
  • Wheat, (Cereal)
  • Chickpeas, (Legume)
  • Lentils, (Legume)
  • Mungbeans, (Legume)

  Region:

  North, South, West

  Project Status: Completed
• GRDC Code: CUR2002-001RTX

  Using Machine Learning To Develop New Methods For Genetic Gain In Crops Challenged By Fungal Diseases

  This project will apply a machine learning approach to genetic and high throughput phenotyping data to draw out genetic markers associated with resistance to fungal infections in wheat, canola, lentil, and chickpea crops.

  Project start date:

  17/02/2020

  Project end date:

  30/06/2022

  Crop type:

  • Wheat, (Cereal)
  • Chickpeas, (Legume)
  • Lentils, (Legume)
  • Canola/Rapeseed, (Oilseed)

  Region:

  North, South, West

  Project Status: Completed
• GRDC Code: UOS2002-002RTX

  Machine learning to map soil constraint variability and predict crop yield

  This project will use a variety of Machine Learning techniques to bring together previously underutilised on-farm, satellite, and weather data and better predict expected crop outcomes. Tools to map fine-scale 3D-variability of agronomically…

  Project start date:

  17/02/2020

  Project end date:

  31/10/2021

  Crop type:

  • Wheat, (Cereal)

  Region:

  North, South, West

  Project Status: Completed
• GRDC Code: AGT2002-002RTX

  Applying machine learning to improve genetic gain delivered from genomic selection in plant breeding

  Machine learning is the next-generation solution to identifying patterns in large datasets and involves using computer science and statistics to analyse very large datasets. These datasets are too complex to uncover with traditional human-led…

  Project start date:

  17/02/2020

  Project end date:

  16/02/2022

  Crop type:

  • Wheat, (Cereal)

  Region:

  North, South, West

  Project Status: Completed
• GRDC Code: UOQ2002-008RTX

  Machine learning applied to high-throughput feature extraction from imagery to map spatial variability

  This project uses Machine Learning to develop high-throughput phenotyping (HTP) of crop canopy features. Plant images from the many project partners train machine learning models for this. The model training is done using the Weiner supercomputer at …

  Project start date:

  17/02/2020

  Project end date:

  30/06/2022

  Crop type:

  • Wheat, (Cereal)
  • Barley, (Cereal)
  • Sorghum, (Cereal)
  • Canola/Rapeseed, (Oilseed)

  Region:

  North, South, West

  Project Status: Completed
• GRDC Code: UOA2001-010BLX

  Improving frost and heat stress management for SA Durum growers

  Durum is of particular importance to SA, over the past 5 years the average area sown has been 60,300ha, producing 158,200 tonnes (Crop and Pasture report). Relative to other cereals the seasonal variation in durum production is greater predominantly …

  Project start date:

  15/01/2020

  Project end date:

  30/06/2020

  Crop type:

  • Wheat, (Cereal)

  Region:

  South

  Project Status: Completed
• GRDC Code: UMU2001-001RTX

  Synchrotron Postdoctoral Fellow no. 4: Plants - Novel foliar fertilisers and nutrition trait diversity of grains

  As global attention shifts towards nutritional food security, the density of key micronutrients (zinc, iron, selenium, iodine and Vitamin A) in Australian cereal grains will increasingly become a key market requirements and potential differentiation …

  Project start date:

  15/01/2020

  Project end date:

  31/10/2024

  Crop type:

  • Wheat, (Cereal)

  Region:

  North, South, West

  Project Status: Completed