Session 6: Optimizing surveillance protocols using unmanned aerial systems

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


Its a tool, not the solution.As with most technologies

How do we integrate and/or optimise?Payloads?


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


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


Sugarcane aphid, Melanaphis sacchari How does data from UAS inform our decision-making?

Prioritize your route:ABCD


Meaningful indices: relating biology to sensor dataNDVIElliott et al. 2015 (JEE)

High SCA

Low SCA


NIR Band Only

3. Physical sample?n = 31P < 0.001


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.


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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


Team membersFelipe GonzalezGrant HamiltonJohn WeisDuncan CampbellGeoff PeggJon KokJim EldridgeQuestions?System

Session 6: Optimizing surveillance protocols using unmanned aerial systems

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Transcript of Session 6: Optimizing surveillance protocols using unmanned aerial systems