session 6: optimizing surveillance protocols using unmanned aerial systems
TRANSCRIPT
SX 2016_UAS 2135
Optimising surveillance protocols using small unmanned aircraft systems (sUAS)Brian P. McCornackAssociate Professor, Kansas State UniversityPlant Biosecurity Cooperative Research Centre
biosecurity built on science
biosecurity built on science
Its a tool, not the solution.As with most technologies
How do we integrate and/or optimise?Payloads?
biosecurity built on science
Point and shoot-Low cost-Light weight-Customised filtersSony A6000Lens config.Dual mount
Specialized sensors-Higher cost per unit-Higher computational requirements and expertiseMultispectralHyperspectralThermal
Data quality and flight stamina-Battery life and payload limitations-Spatial resolution / accuracyBatteryTopCon (GPS)
biosecurity built on scienceOptimization of currently available sensors4
Obstacle Avoidance
Vision positioning
More accessible ($1600 AUD)
Technology is a Moving TargetHyperspec: $400K down to $80K
biosecurity built on science1. When and where to fly?
Bayesian Belief NetHamilton & Eldridge
Include utility function to help decide when (if) to deploy UAS
Optimise utility function to minimise costs, maximise benefits
Risk of stripe rust outbreak
biosecurity built on scienceRisk model used to inform the when and whereAim to optimise the use of UAS to provide benefit for management of fungal crop diseasesModel risk of fungal disease outbreak, link to decision to deploy UAS.
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1. When and where to fly?
Preliminary Results (Novel fungal risk modeling method)Susceptible and occasionally moderately susceptible cultivars lead to high risk situations (p>50%)
Crop losses under low risk (low risk >50%) are less than operational UAS costs. Therefore no economic benefits of UAS deployment under low outbreak risk (p (low risk) > 50%)
Consider deployment when high risk of crop losses (p (high risk) => 25%)Hamilton & Eldridge
biosecurity built on scienceRisk model used to inform the when and whereAim to optimise the use of UAS to provide benefit for management of fungal crop diseasesModel risk of fungal disease outbreak, link to decision to deploy UAS.
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2. Areas of interest?
Myrtle rust field experiment in NSW, managed by Dr. Geoff PeggGonzalez & Kok
biosecurity built on scienceMyrtle Rust NDVI in Tea Tree
NDVI 1 (red) = high biomassNDVI 0 (blue) = low biomass
Trees look similar in normal RGB (left) camera but more distinct vegetation characteristics can be identified when using other spectral information, e.g., NDVI, which uses the NIR band.NDVI = Normalized Difference Vegetation Index (biomass)1.00.0RGBNDVI
Gonzalez & Kok
biosecurity built on science
Myrtle RustHyperspectral signature
Red line = no myrtle rustBlue line = myrtle rust
Trees infected with myrtle rust are absorbing lesser visible light (400 to 660 nm) for photosynthesis than uninfected treesWavelength (400 to 1000 nm)ReflectanceHealthyInfectedGonzalez & Kok
biosecurity built on scienceDetection experiment simulated disease in vineyards
How good are we at finding pests?
90% chance of detecting pest have to spend at least 2 sec/vineTotal time for vineyard = 9 hours
Weiss
biosecurity built on science
Sugarcane aphid, Melanaphis sacchari How does data from UAS inform our decision-making?
Prioritize your route:ABCD
biosecurity built on science
Meaningful indices: relating biology to sensor dataNDVIElliott et al. 2015 (JEE)
High SCA
Low SCA
biosecurity built on science
NIR Band Only
3. Physical sample?n = 31P < 0.001
biosecurity built on science
Grasp
Drop
SuctionUAS & multitasking
4. Practical? (Short- and long-range integration)spore trapping
pheromone, baits, sterile males, etc.
data
Caretaker (autonomous ground vehicle)
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Farmers / consultantsExtension agentsBiosecurity personnelState agencies Federal agencies (FAA, CASA)Industry
Who will benefit from the research?
UAS Summit (Kansas DOT, October 2015, n = 58)
Relevant research areas:Data analysis (19%)Services (18%)Aerial platforms (17%)
Relevant applications:Biosecurity (18%)Infrastructure (18%)Public safety (17%)
Strong mission statement:Strongly agreed (>72%) that we needed to define one for UAS in KS
State legislation priorities:Industry growth (33%)Job creation (23%)Public education (21%)
biosecurity built on scienceDelivery to end-users?
Case Study #1Adaptive decision treesPublication on best management practices for UAS in realm of plant biosecurity and pest management in generalTraditional methodsPeer reviewed publications, presentations, workshops, etc.
biosecurity built on science
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How will it benefit end-users?
Increasing the probability of early detection rateSurveillance deployment strategies, site-specific management, etc.Reduced surveillance costMore efficient deployment strategies (e.g., less time, using fewer resources, covering more area)Building capacity and capabilityPrimary education: accessible lesson plans, science and technologyPilots (exemptions and teaching certificates) to scientists (postdocs)Barriers around use, provide input to regulatory processesAccess to UAS expertise within the context of plant biosecurityNew tools and a validated pathway to deploymentfaster, cheaper, and more confident decision-making
biosecurity built on scienceUAS removes the need for people to enter a production areadirectly addresses a key biosecurityissue of how to investigate a suspect without spreading it further.
The ability to provide a landscape viewor new perspective across a production area in real timeallows for more targeted surveillance usingvisual symptoms.
If this can be further enhanced through sampling of those suspects using UAS, the result maybequicker surveillance covering alarger area using less resources.End-users perspective (Dr. Louise Rossiter)
Leader Plant Pest Surveillance, Department of Primary Industries
biosecurity built on science
Team membersFelipe GonzalezGrant HamiltonJohn WeisDuncan CampbellGeoff PeggJon KokJim EldridgeQuestions?System