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Intelligent Systems for Commercial Application in Perennial Horticulture †

Everard J. Edwards
1,* and
Peyman Moghadam
CSIRO Agriculture & Food, Pullenvale, QLD 4069, Australia
CSIRO Data 61, Pullenvale, QLD 4069, Australia
Authors to whom correspondence should be addressed.
Presented at the third International Tropical Agriculture Conference (TROPAG 2019), Brisbane, Australia, 11–13 November 2019.
Proceedings 2019, 36(1), 59;
Published: 17 January 2020
(This article belongs to the Proceedings of The Third International Tropical Agriculture Conference (TROPAG 2019))


Production in perennial horticulture relies on a high degree of crop management, but, due to that perenniality, management decisions need to balance short- and long-term impacts. Optimising these decisions requires information about the plants and it requires that information at multiple time-points. The development of intelligent systems, based on new technologies and new data analytics that take advantage of always available high-performance edge computing, provide a unique opportunity to create a step-change in the management of perennial horticulture crops. For example, combining LiDAR (3D laser imaging) with simultaneous localization and mapping (SLAM) enables the capture of 3D canopy structure on a per tree basis at the orchard scale. Vegetation indices like light penetration, light distribution or foliage density can be estimated directly, in real-time, without a labour-intensive process. Overlaying such an analysis with the output of other sensing modalities extends their application to provide real time, on-farm, decision support by monitoring the condition of every plant in 3D. Even consumer RGB video cameras provide a resolution and frame-rate adequate for a wide range of applications when combined with computer-based image segmentation and machine learning techniques. Such technologies offer the prospect of imaging and analysing a future orchard at any phenological time-point and having a block-level result for the parameter of interest, together with the spatial variability data that will assist in long-term management decisions. In this presentation we will provide examples of these technologies, their current application and how they will be utilised in a future orchard system.

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MDPI and ACS Style

Edwards, E.J.; Moghadam, P. Intelligent Systems for Commercial Application in Perennial Horticulture. Proceedings 2019, 36, 59.

AMA Style

Edwards EJ, Moghadam P. Intelligent Systems for Commercial Application in Perennial Horticulture. Proceedings. 2019; 36(1):59.

Chicago/Turabian Style

Edwards, Everard J., and Peyman Moghadam. 2019. "Intelligent Systems for Commercial Application in Perennial Horticulture" Proceedings 36, no. 1: 59.

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