NCEA's Remote Monitoring and Automatic Detection of Grain Crop Attributes for GRDC Variety Trials
Dataset
Dataset/Collection Name | NCEA's Remote Monitoring and Automatic Detection of Grain Crop Attributes for GRDC Variety Trials |
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Type | Dataset |
Data Description | The National Variety Trials (NVT) involve a yearly coordination of 630 grain trials conducted across 250 locations in Australia. At different stages of the crop season Trial Service Providers visually assess the attributes of the grain plants in each trial-plot to evaluate the growth and development of the different grain varieties. This involves manual measurements related to: (i) plant dimensions (height, canopy size); (ii) different stages of growth (seedling, tillering, jointing, boot and flowering); and (iii) germination rate. However, the availability of personnel to perform this monitoring is likely to be constrained to larger research stations. These plant attributes can be visually monitored and automatically detected using remote camera-based machine vision technologies to improve the timeliness and consistency of assessment of the grain varieties. In addition to streaming visual data of the crop, there is potential for machine vision technology to automatically analyse the images to determine a range of plant attributes and performance indicators from video-frame samples collected daily; such as flowering behaviour (50% of the plot to anthesis) and crop height. The data captured, processes and stored will be used to determine variation between varieties of grains across Australia. |
Research Involvement | |
Is owned by | Tscharke, Matthew |
Physical Storage Location | |
USQ Toowoomba | Toowoomba |
Geographic Location | Australian Continent Geographic Location Bounding Box (-43.6345972634, 113.338953078) to (-10.6681857235, 153.569469029) |
Digital Object Identifier (DOI) | https://doi.org/10.26192/ttq9-v179 |
ANZSRC Field of Research 2020 | 300207. Agricultural systems analysis and modelling |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q6501/ncea-s-remote-monitoring-and-automatic-detection-of-grain-crop-attributes-for-grdc-variety-trials
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