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.

In recent times, multi-spectral drone imagery has proved to be a useful tool for measuring tree crop canopy structure. In this context, establishing the most appropriate flight planning variable settings is an essential consideration due to their controls on the quality of the imagery and derived maps of tree and crop biophysical properties. During flight planning, variables including flight altitude, image overlap, flying direction, flying speed and solar elevation, require careful consideration in order to produce the most suitable drone imagery. Previous studies have assessed the influence of individual variables on image quality, but the interaction of multiple variables has yet to be examined. This study assesses the influence of several flight variables on measures of data quality in each processing step, i.e. photo alignment, point cloud densification, 3D model building, and ortho-mosaicking. The analysis produced a drone flight planning and image processing workflow that delivers accurate measurements of tree crops, including the tie point quality, densified point cloud density, and the measurement accuracy of height and plant projective cover derived from individual trees within a commercial avocado orchard. Results showed that flying along the hedgerow, at high solar elevation and with low image pitch angles improved the data quality. Optimal flying speed needs to be set to achieve the required forward overlap. The impacts of each image acquisition variable are discussed in detail and protocols for flight planning optimisation for three scenarios with different drone settings are suggested. Establishing protocols that deliver optimal image acquisitions for the collection of drone data over horticultural tree crops, will create greater confidence in the accuracy of subsequent algorithms and resultant maps of biophysical properties.