A Multifaceted Approach to Developing an Australian National Map of Protected Cropping Structures

Andrew Clark (), Craig Shephard, Andrew Robson, Joel McKechnie, R. Blake Morrison and Abbie Rankin
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Andrew Clark: Applied Agricultural Remote Sensing Centre, The University of New England, Armidale, NSW 2350, Australia
Craig Shephard: Applied Agricultural Remote Sensing Centre, The University of New England, Armidale, NSW 2350, Australia
Andrew Robson: Applied Agricultural Remote Sensing Centre, The University of New England, Armidale, NSW 2350, Australia
Joel McKechnie: Applied Agricultural Remote Sensing Centre, The University of New England, Armidale, NSW 2350, Australia
R. Blake Morrison: Applied Agricultural Remote Sensing Centre, The University of New England, Armidale, NSW 2350, Australia
Abbie Rankin: Applied Agricultural Remote Sensing Centre, The University of New England, Armidale, NSW 2350, Australia

Land, 2023, vol. 12, issue 12, 1-22

Abstract: As the global population rises, there is an ever-increasing demand for food, in terms of volume, quality and sustainable production. Protected Cropping Structures (PCS) provide controlled farming environments that support the optimum use of crop inputs for plant growth, faster production cycles, multiple growing seasons per annum and increased yield, while offering greater control of pests, disease and adverse weather. Globally, there has been a rapid increase in the adoption of PCS. However, there remains a concerning knowledge gap in the availability of accurate and up-to-date spatial information that defines the extent (location and area) of PCS. This data is fundamental for providing metrics that inform decision making around forward selling, labour, processing and infrastructure requirements, traceability, biosecurity and natural disaster preparedness and response. This project addresses this need, by developing a national map of PCS for Australia using remotely sensed imagery and deep learning analytics, ancillary data, field validation and industry engagement. The resulting map presents the location and extent of all commercial glasshouses, polyhouses, polytunnels, shadehouses and permanent nets with an area of >0.2 ha. The outcomes of the project revealed deep learning techniques can accurately map PCS with models achieving F-Scores > 0.9 and accelerate the mapping where suitable imagery is available. Location-based tools supported by web mapping applications were critical for the validation of PCS locations and for building industry awareness and engagement. The final national PCS map is publicly available through an online dashboard which summarises the area of PCS structures at a range of scales including state/territory, local government area and individual structure. The outcomes of this project have set a global standard on how this level of mapping can be achieved through a collaborative, multifaceted approach.

Keywords: protected cropping; greenhouses; nets; remote sensing; geographical information systems; deep learning; aerial imagery; satellite imagery (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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