Procurement Summary
Country : Australia
Summary : Project D : Enabling analytics for grain crop monitoring applications - Crop classification and mapping
Deadline : 19 Apr 2019
Other Information
Notice Type : Tender
TOT Ref.No.: 31592490
Document Ref. No. : PROC-9175879
Competition : ICB
Financier : Self Financed
Purchaser Ownership : -
Tender Value : Refer Document
Purchaser's Detail
Purchaser : GRAINS RESEARCH AND DEVELOPMENT CORPORATION
Cindy Hall
P : +61 8 8198 8401
E : Southern@grdc.com.au
W : https : //grdc.com.au/research/applying-and-reporting/current-procurement/open-tenders
Document(s) Contact Name : Cindy Hall
Document(s) Contact Phone : +61 8 8198 8401
Document(s) Contact Email : Southern@grdc.com.au
Australia
Email :Southern@grdc.com.au
Tender Details
!--[if !supportLists]--Project D : Enabling analytics for grain crop monitoring applications - Crop classification and mapping of phenological characteristics.
This procurement is part of a tender consisting of five interrelated procurements. Applicants may apply for one, all or any combination of the ‘Enabling analytics for grain crop monitoring applications' procurements listed. For further information visit : https : //grdc.com.au/research/applying-and-reporting/current-procurement/open-tenders.
With the required metadata, crop phenological characteristics such as the timing of emergence and physiological growth stage information (i.e. anthesis) can be used as key data inputs for applications in crop modelling to support variety and sowing time decisions, yield forecasting, crop input management strategies and the management of production risk.
Remote sensing platforms provide a basis for crop species classification and the assessment of vegetation dynamics throughout a crop developmental cycle (Zhang et al. 2003, Bargiel 2017). Recent studies have developed workflows to downscale MODIS imagery via fusion with lower frequency but higher spatial resolution Landsat 8 data for crop phenology mapping at a 30m spatial resolution (Gao et al. 2015, Onojeghuo et al. 2018). A 2017 study similarly used MODIS-derived NDVI time series data to assess the extent of variability in spatio-temporal growth patterns over a 15-year period across distinct management zones for applications in precision agriculture at a study site in Minnipa, South Australia (Araya et al. 2017).
Existing knowledge on relative crop susceptibility to pests, diseases and abiotic constraints at different development stages could be leveraged against highly frequent, spatially-referenced measures of crop developmental stage at the paddock-scale to improve the crop-modelling applications underpinning various decision support tools. The spatial and temporal quantification of crop species and developmental stage using remotely sensed imagery could provide an enabling data layer by which to better understand G x E x M interactions on a larger scale to deliver improved products and services to Australian grain growers that enable more profitable decision making.
For full details on the ATM including outcome, outputs, evaluation critera, associated attachments and information on any Questions and Aswers relating to the ATM, visit : https : //grdc.com.au/research/applying-and-reporting/cu...
Category : 70140000 - Crop production and management and protection
Close Date & Time : 19-Apr-2019 2 : 00 pm (ACT Local Time)
Documents
Tender Notice