HORIZON SCAN 2 - Agrifutures Australia...telematics autonomous_vehicles customer_journey_analytics...

1 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY

HORIZON SCAN 2APRIL, 2017

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© 2017 Rural Industries Research and Development Corporation. All rights reserved.

ISBN 978-1-74254-956-9 ISSN 1440-6845

Horizon Scan 2 Publication No. 17/033 Project No. PRJ-010512

The information contained in this publication is intended for general use to assist public knowledge and discussion and to help improve the development of sustainable regions. You must not rely on any information contained in this publication without taking specialist advice relevant to your particular circumstances.

While reasonable care has been taken in preparing this publication to ensure that information is true and correct, the Commonwealth of Australia gives no assurance as to the accuracy of any information in this publication.

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This publication is copyright. Apart from any use as permitted under the Copyright Act 1968, all other rights are reserved. However, wide dissemination is encouraged. Requests and inquiries concerning reproduction and rights should be addressed to RIRDC Communications 02 6923 6900.

Researcher Contact Details

Name: Dr Grant Hamilton Address: QUT, GPO Box 2434, Brisbane, QLD 4001

Phone: +61 3138 2318 Email: [email protected]

In submitting this report, the researcher has agreed to RIRDC publishing this material in its edited form.

RIRDC Contact Details

Rural Industries Research and Development Corporation Building 007 Charles Sturt University Boorooma Street Wagga Wagga NSW 2650

C/- Charles Sturt University Locked Bag 588 Wagga Wagga NSW 2678

Phone: 02 6923 6900 Fax: 02 6923 6999 Email: [email protected]. Web: http://www.rirdc.gov.au

Electronically published by RIRDC in August 2017 Print-on-demand by Union Offset Printing, Canberra at www.rirdc.gov.au or phone 1300 634 313

http://www.rirdc.gov.au/

Horizon Scan 2: April, 2017. Report by Queensland University of Technology for Rural

Industries Research and Development Corporation, Australia.

REPORT CONTRIBUTORSDr Grant Hamilton, Dr Levi Swann, Dr Sangeetha Kutty, Prof Greg Hearn, Assoc Prof

Richi Nayak, Dr Markus Rittenbruch, Prof Roger Hellens, Dr Debra Polson, and Dr Jared

Donovan.

ACKNOWLEDGEMENTSThe research provider Queensland University of Technology acknowledges the financial

assistance of the Rural Industries Research and Development Corporation in order to

undertake this project.

The contributions of members of the Australian rural industries, who gave their time to

participate in surveys as part of this research, are also acknowledged.


TABLE OF CONTENTS

Executive Summary

Introduction

Approach

Results

Digital Twin

Low Power Wide Area Networks

Human-Machine Interfaces

Solar Retransmission

Personal Analytics

Conclusion

References

Appendix: Approach and Project Streams

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EXECUTIVE SUMMARY

Technology advancement and technology driven disruption is expected to experience

exponential growth in the coming decades. Although Australian agriculture has a strong

track record of technology integration, the speed of technology innovation makes the

scanning and identification of high impact emerging technology a priority for Australian

rural industries. This report responds to this priority, and presents the outcomes of a

methodology to scan, synthesise and communicate technology trends and innovations

that may impact Australian rural industries.

In this first iteration, a broad range of category and discrete technology types have

been identified. Led by data mining and synthesis, and assisted by QUT experts and

innovative individuals identified by RIRDC, a sub-set of these technologies has been

refined. Presented in this report are the most highly rated technologies, positioned at the

confluence and fringes of technology megatrends including the Internet of Things, big

data, artificial intelligence and advanced sensors.

DIGITAL TWINA digital twin is a dynamic software model that represents a physical asset or process.

Generated by sensors installed in physical assets and processes, a digital twin allows a

user to view and act on real-time data remotely. Simulations can be run using a digital

twin to predict asset or process failure. Increasing implementation of smart devices

means that digital twins can potentially represent highly complex processes and be

transformative in nearly all industries.


LOW POWER WIDE AREA NETWORKSLow power wide area networks (LPWAN) support communication between sensors

and smart devices. We have highlighted a type of LPWAN called LoRaWAN.

LoRaWAN technology uses unlicensed radio frequencies and therefore can support

the interoperability of smart devices and sensors without reliance on cellular networks.

This positions LoRaWAN as a significant technology for supporting Internet of Things

systems in remote locations.

HUMAN-MACHINE INTERFACESHuman-machine interfaces enable communication between humans and machines. We

have highlighted three types of human machine interfaces. Natural language interfaces

utilise sensory and non-sensory inputs such as touch, sight, and radar to facilitate

communication between humans and machines. Conversational interfaces enable

communication between humans and machines through spoken or written language.

Wearable user interfaces allow communication between humans and machines through

input collected from the user or environment such as fatigue and location. Innovative

human-machine interfaces have the potential to remove complexity from interactions

with machines, allowing unskilled workers with poor computer literacy to perform

complex tasks.

SOLAR RETRANSMISSIONSolar retransmission is a patented technology for using satellite technology to capture

and retransmit sunlight for use on Earth. The sunlight is captured in space and then

retransmitted in frequency bands optimal for photosynthesis. This technology is viewed

as an expansion on the capabilities of precision agriculture and sits alongside proposals

for lunar-based solar energy and mineral collection.

PERSONAL ANALYTICSPersonal analytics technologies monitor personal activity and behaviour. Two

implementations of personal analytics technologies are presented. Labour tracking

leverages personal analytics technologies, such as wearable sensor devices, to track

worker fatigue, stress, and movement. Smart contact lenses are an emerging personal

analytics technology. Worn just like normal contact lenses, they are capable of monitoring

biometric signals, as well as enhancing vision with augmented reality. Smart contact

lenses represent the emergence of nanosensors for personal analytics and labour

tracking. The innovation of unobtrusive wearable devices that use nanosensors is

potentially transformative in a range of domains.

Successive iterations of the methodology will be employed in future reporting periods to

supplement the already identified technologies. Findings will continue to be extrapolated

to clearly communicate the transferability and subsequent impact of key emerging

technologies for Australian rural industries.


INTRODUCTION

In this project, we are developing a scanning capability to assist the early detection,

analysis and forecasting of issues and opportunities that may impact Australian rural

industries. This will support rural industries and the range of supporting leaders and

organisations to make sound policy and investment decisions that will position Australian

rural industries well into the future.

Horizon Scan 1 worked toward the project aims by developing and then implementing

a horizon scanning method comprising of interdisciplinary expertise, data mining and

visualisation. This method was used to extract and synthesise potential issues and

opportunities in the context of five broad focus areas representing the imperatives of

Australian rural industries. The outcomes of Horizon Scan 1 were directed toward one of

these focus areas, market and consumer preferences, and resulted in the identification

of seven key trends.

Horizon Scan 2 incorporates learnings from Horizon Scan 1, and builds on the capabilities

developed. Emphasis has been placed on revising the approach to accentuate trend

discovery, evaluation and consolidation. Perhaps most significantly, Horizon Scan 2

adopts a refined scope, drawing on the previously identified megatrend of transformative

technologies. The Rural Industries Futures Megatrends report outlined that transformative

technologies will have significant impact on almost all facets of Australian rural and

agriculture industries (Hajkowicz & Eady, 2015). While Australian agriculture has a

track record for successful and timely adoption of technology, technology advancement

and disruption is likely to experience exponential growth over the coming decades. The


speed at which this disruption occurs makes the timely identification of technologies and

the analysis of their potential impact on Australian rural industries particularly important.

In each of the reports produced during this project, we look to supplement the

knowledge base already established in the Rural Industries Futures Megatrends report,

and subsequent technology factsheets developed by RIRDC. Specifically, we will target

emerging technologies that are impacting a range of industries in an international

context. This report represents the first iteration of this direction. Given the shift in

scope and revision of methodology, the following section presents a brief outline of the

approach used for Horizon Scan 2 before presenting the technologies identified during

this reporting period.


APPROACH

This project employs a concurrent phase, iterative methodology to:

1. scan and extract data from a variety of sources;

2. synthesise this information to identify potential trends and innovations for further

investigation;

3. and, communicate these issues through visualisations.

Implementation of each respective project phase, led by data mining, synthesis, and

visualisation, occurs across three interactive streams: Discovery; Evaluation; and,

Consolidation. These streams are continuous over the course of the project requiring

different inputs from each project phase. The inputs from each phase, and their

interactivity is represented in Figure 1. A more detailed description of this approach is

included in the Appendix: Approach and Project Streams.

DISCOVERYImplementing data mining and human synthesis, the discovery stream focused on

scanning selected data sources, including twitter, patent databases, industry and market

reports, and technology news media. The confluence of data mining and synthesis

outputs in the discovery stream resulted in a list of emerging technologies for further

investigation in the subsequent evaluation and elaboration streams.


EVALUATIONLeveraging QUT experts and innovative farmers identified by RIRDC, the evaluation

stream focused on filtering the signals identified in the discovery stream. This was

achieved using online surveys in which experts were asked to rate the potential impact

that a broad range of emerging technologies will have on Australian rural industries.

This process has assisted us to narrow the scope of technologies we have presented

in this report, and that will be included on the watchlist for monitoring throughout future

reporting periods.

CONSOLIDATIONThe consolidation stream focused on further investigating and monitoring technologies

on the initial watchlist. This involved a detailed analysis of the technologies’ potential

impact for Australian rural industries and developing a rationale for why they should be

monitored. Both data mining and synthesis are essential for this stream to receive input

from a broad range of data, and gain the requisite detail. Visualisation is used to draw

together and present inputs from data mining, synthesis, and evaluations provided by

experts.

FIGURE 1. PROJECT INPUTS AND STREAM INTERACTION

Sourc

es

Data

minin

g

List o

f tech

nolog

ies

Surve

y

Sourc

es

Surve

y

Synth

esis

Synth

esis

Data

minin

g

Synth

esis

List o

f tech

nolog

ies

Surve

y res

ults

DISCOVERYEVALUATIONCONSOLIDATION

Visua

lisati

on

Synth

esis

Data

minin

g


iot

sensorsnetworks

big_data

artificial_intelligenceadvanced_materials

automation

cloud_computing

data_analytics

renewable_energy

ambient_intelligence

digital_health

genomics

machine_learning

cybersecurity

nanotechnology

wearables

ai

automated_finance

quantified_self

computer_vision

human2machine

imagery

industry_4.0

telecommunication

3d_printing

immersive_technology

neural_networks

personalised_medicine

predictive_analytics

synthetic_foods

telematics

autonomous_vehicles

customer_journey_analytics

deep_learning

digital_business

digital_finance

digital_mesh

future_of_food

infomatics

information_modelling

labour_tracking

mobile_dominance

natural_language_interface

natural_language_interfaces

neuromorphic_hardware

pattern_recognition

personal_analytics

sattelites

transportation

4d_printing

adaptive_security_architecture

affective_computing

ambient_user_interfaces

app_economy

assisted_selection

augmented_reality

aware_computing

bg_data

biomarkers

biometrics

biosensing

bitcoin

blockchain

botnets

brain

broadband_capable_sattelites

caavo

cell_cultured_milk

coating_for_frost_prevention

cognitive_computing

collaborative_robots

computer_interface

context

context_brokering

conversational

cryptocurrency

cyber_defence

cyber_insurance

dark_data

data_brokering

data_governance

data_storytelling

data_virtualisation

device_mesh

dig_data

digital_payments

digital_twin

distributed_ledger

driverless_vehicles

drone_delivery

energy_farming

future_of_work

genetic_modification

genome_medicine

genome_sequencing

graphene

green_crude

heuristic_automation

human_augmentation_technology

hyperloop

ibm_watson

industrial_iot

infonomics

infosecurity

insect_protein

intelligent_apps

internet_of_health

ion_batterieslong_range_wide_area_network

machine2machine

machine2machine_communication

machine_intelligence

marker

mesh_app_and_service_architechture

microbiome

mixed_reality

mobile_technology

modelling

modile_dominance

nanotechnology_energy_generator

nci_cloud

neromorphic_computing

network_ubiquity

networks_

neuromorphic_computing

novel_materials_for_3d_printing

organic_solar_cells

personl_analytics

platforms

precision_medicine

predictive

printed_nanotechnology

robotics

sattelite_sun_capture

shadow_it

smart_cities

smart_contact_lenses

smart_dust

smart_farm

smart_grid

smart_home

smart_workplace

sodium

software_innovation

space_exploration_and_presence

speech_analytics

speech_recognition

stem_cells

synthetic_biology

synthetic_wine

systems_medicine

tech_sequencing

tellecommunication

tomato_genome_sequencing

unmanned_aerial_vehicle

vehicular_network

vertical_farming

virtual_reality

voice_recognition

volumetric_displays

wearable_user_interface

wireless_electric_vehicle_charging

wireless_unification

RESULTS

FIGURE 2. VISUALISATION OF DISCOVERY STREAM OUTPUT


iot

sensorsnetworks

big_data

artificial_intelligenceadvanced_materials

automation

cloud_computing

data_analytics

renewable_energy

ambient_intelligence

digital_health

genomics

machine_learning

cybersecurity

nanotechnology

wearables

ai

automated_finance

quantified_self

computer_vision

human2machine

imagery

industry_4.0

telecommunication

3d_printing

immersive_technology

neural_networks

personalised_medicine

predictive_analytics

synthetic_foods

telematics

autonomous_vehicles

customer_journey_analytics

deep_learning

digital_business

digital_finance

digital_mesh

future_of_food

infomatics

information_modelling

labour_tracking

mobile_dominance

natural_language_interface

natural_language_interfaces

neuromorphic_hardware

pattern_recognition

personal_analytics

sattelites

transportation

4d_printing

adaptive_security_architecture

affective_computing

ambient_user_interfaces

app_economy

assisted_selection

augmented_reality

aware_computing

bg_data

biomarkers

biometrics

biosensing

bitcoin

blockchain

botnets

brain

broadband_capable_sattelites

caavo

cell_cultured_milk

coating_for_frost_prevention

cognitive_computing

collaborative_robots

computer_interface

context

context_brokering

conversational

cryptocurrency

cyber_defence

cyber_insurance

dark_data

data_brokering

data_governance

data_storytelling

data_virtualisation

device_mesh

dig_data

digital_payments

digital_twin

distributed_ledger

driverless_vehicles

drone_delivery

energy_farming

future_of_work

genetic_modification

genome_medicine

genome_sequencing

graphene

green_crude

heuristic_automation

human_augmentation_technology

hyperloop

ibm_watson

industrial_iot

infonomics

infosecurity

insect_protein

intelligent_apps

internet_of_health

ion_batterieslong_range_wide_area_network

machine2machine

machine2machine_communication

machine_intelligence

marker

mesh_app_and_service_architechture

microbiome

mixed_reality

mobile_technology

modelling

modile_dominance

nanotechnology_energy_generator

nci_cloud

neromorphic_computing

network_ubiquity

networks_

neuromorphic_computing

novel_materials_for_3d_printing

organic_solar_cells

personl_analytics

platforms

precision_medicine

predictive

printed_nanotechnology

robotics

sattelite_sun_capture

shadow_it

smart_cities

smart_contact_lenses

smart_dust

smart_farm

smart_grid

smart_home

smart_workplace

sodium

software_innovation

space_exploration_and_presence

speech_analytics

speech_recognition

stem_cells

synthetic_biology

synthetic_wine

systems_medicine

tech_sequencing

tellecommunication

tomato_genome_sequencing

unmanned_aerial_vehicle

vehicular_network

vertical_farming

virtual_reality

voice_recognition

volumetric_displays

wearable_user_interface

wireless_electric_vehicle_charging

wireless_unification

For Horizon Scan 2, we present results for one cycle of each the discovery, evaluation and

synthesis streams. Data mining and synthesis outputs from the discovery stream have

led to the identification of a broad range of technologies. These identified technologies

can be classified as either category technologies or discrete technologies.

Category technologies describe broad classifications of technology, while discrete

technologies describe specific implementations of technology, and they are generally

related to one or more category technology. Examples of the relationship between

category and discrete technologies is shown in Figure 2, which is a visualisation of a

segment of the discovery stream outcome. Category technologies, such as Internet of

Things, sensors and networks are represented by large nodes. These large category

technology nodes share many connections with discrete technologies, which are

represented by the smaller nodes.

The category and discrete technologies identified during the discovery phase form the

initial pool from which we have selected technologies for evaluation and consolidation. For

the Horizon Scan 2 reporting period, a range of discrete and category technologies were

selected for further investigation. The remaining pool of technologies will be investigated

in successive cycles of the methodology. Moreover, as the project continues, successive

cycles of the discovery stream will identify and introduce new technologies for further

evaluation. Select technologies that are evaluated to have high potential impact will be

monitored to track their development over the course of the project.


DIGITAL TWIN

A digital twin is a dynamic software model of a physical thing or system (Cearley, Walker,

& Burke, 2016). It is connected to and uses data from sensors that are installed in

the physical object or system that is being represented (Feuer & Weissman, 2016).

In addition to using data from sensors integrated in physical things, digital twins can

integrate contextual data, including metadata, condition or state data such as location

and temperature, event data, and analytics in the form of algorithms and rules (Cearley

et al., 2016). By utilising this data, a digital twin can represent the structure, context and

behaviour of a physical thing or system in digital space.

APPLICATIONDigital twins provide an interface that allows a user to understand past and present

operation of a thing or system (Feuer & Weissman, 2016), and then respond to

any changes as necessary (Cearley et al., 2016). As data is collected over time, the

application of a digital twin becomes more powerful and allows for greater intelligence

within a system. For example, data collected over time can be used to monitor equipment

and predict part failure, thus enabling a shift from a preventative maintenance model to

condition-based asset management (Cearley et al., 2016). Beyond this, a digital twin

can be used to virtually simulate, make predictions about, validate, and optimise an entire

production system’s future operations (Feuer & Weissman, 2016; Volkmann, 2016)

Digital twins have typically been used in high-value asset industries, such as manufacturing

and transportation (Velosa, Schulte, & Lheureux, 2016). However, the very idea of a

digital twin and its capabilities is shifting with the advancement of the Internet of Things

space. Offering the ability to digitise physical space, a digital twin provides a bridge

between the physical and the digital world (Volkmann, 2016). General Electric is driving

digital twin innovation, and has created a digital interface for managing a wind farm.

The interface represents digital equivalents of each wind turbine in the wind farm, and

provides various information about the operating environment and conditions of the

wind turbines. Controls are also present on the interface that allow configurations to be

made to optimise performance (Lund et al., 2016).

FORECASTIn the coming years, sensors and ‘intelligence’ will increasingly be present in common

assets such as buildings, cars, and consumer products, and digital twins will exist for

potentially billions of these things. Given this, digital twins have the potential to represent

complex networks and systems, and to impact nearly all industries (Cearley et al., 2016).


INNOVATION TRENDS

View real-time data on the working condition of equipment

Predict and monitor the conditions under which equipment might fail or break down

Virtually simulate, validate and optimise an entire system or process

EXPERT OPINION

WEARFUEL

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WEAROUTPUT

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STOREPOWER

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TOTAL INPUT

WEARFUEL

USA

GERMANY

GENERAL ELECTRIC

SIEMENS

Experts were surveyed and asked to rate the impact of digital twin technology on a 5-point scale. On average, responses show that the capabilities offered by digital twin technology were perceived to have high impact for Australian rural industries. Particularly highly rated were the capabilities to view real-time data on the working condition of equipment, and to predict and monitor the conditions under which equipment might fail.

DIGITAL TWIN

Digital twin technology is driven by General Electric and Siemens. Digital twin innovation is likely to expand with the growth of the Internet of Things.

Digital twin technology has the potential to be implemented in agricultural processes to monitor equipment status, such as part wear and power input/output, and to test and facilitate optimal system performance.

Digital twin technology is at the confluence of advanced sensor technology, big data, and the Internet of Things. With assets in the real world represented by digital twins, it is possible for a user to monitor and control assets remotely. Over time, and with extensive data, digital twins have the potential to be transformative in many industries. They can be used to predict asset failure, or to simulate changes to a process before implementing them in the real world.

0Very low impact

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Very high impact5

95%98%27%

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370KWh21% 120KWh

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4.44.2


LOW POWER WIDE AREA NETWORKS

Low power wireless area networks (LPWAN) support low bandwidth communication

between sensors and smart devices (Velosa et al., 2016). Various LPWAN technologies

are available and in development, including LoRa, Sigfox, Weightless, Ingenu RPMA,

EC-GSM and NB-IoT. Due to the low bandwidth and subsequent low data transfer of

LPWANs, connected devices can be deployed across large areas and powered for

several years on a single battery (Adelantado et al., 2017).

APPLICATIONApplications enabled by LPWANs depend on device data transfer needs and the size

of the area in which they operate. Commercial LPWANs support the largest areas. The

subscription service, Sigfox, has stations around the world that support up to a 50km

range. NB-IoT and EC-GSM leverage existing licensed mobile network bands and will offer

coverage similar to cellular networks (Wixted et al., 2017). Use cases for such networks

include geotracking of vehicles and communication with geographically dispersed assets,

such as oil pumps and pipelines (Velosa et al., 2016). Other technologies, such as LoRa,

use unlicensed ISM bands, and can therefore be used to set up private networks that are

independent of a network operator (Velosa et al., 2016). This is an important capability

for applications such as agriculture which might be situated in areas not serviced by

reliable wireless connectivity.

LoRaWANs are created by gateways which relay messages between devices and a

central network server. Gateways have a range up to 15km in unobstructed environments

and several gateways can be deployed to cover a greater distance. LoRa technology

facilitates bi-directional communication and can support thousands of devices with

maximum data transfer of 27 kbps. It is best suited to applications that receive and send

a reduced number of regular or irregular messages. This makes it an attractive option for

applications such as agriculture, leak detection, and environmental control. LoRaWANs

are not suitable for industrial automation or critical infrastructure that require real-time

data transfer and monitoring (Adelantado et al., 2017).

FORECASTLoRa has a strong community in urban Europe, however, is less established in other

parts of the world. As an immature market, the LPWAN landscape is likely to change

with the roll-out of commercial networks and an increasing uptake of Internet of Things

for business and consumer applications. While commercial technologies are likely to

dominate the market, smaller players such as LoRa are likely to be persistent to support

specific applications.


0Very low impact

1

2

3

4

Very high impact5

Seamless interoperability among smart things without the need for complex local infrastructure

Inexpensive, low power networking of connected smart things without the need for broadband or mobile internet

EXPERT OPINION

LoRaWANGATEWAY

LoRaWANGATEWAY

Experts were surveyed and asked to rate the impact of LoRaWAN technology on a 5-point scale. On average, responses show that there is a strong interest in smart things, and the Internet of Things in Australian rural industries. Low power and inexpensive networks that are not reliant on complex local infrastructure were perceived to have high impact by participants.

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Map showing the key centres for LoRaWAN networks and number of gateways established in each location.

Proposed uses of LoRaWAN technology in agriculture have been cattle tracking and soil quality monitoring. The application of LoRaWAN technology could extend to a variety of applications that use low power sensors and smart things.

LoRaWANLoRaWAN, and other LPWAN technology is a response to the growing Internet of Things. LoRaWAN technology is capable of providing low power, wide area networks for thousands of connected smart things. Of particular utility is that private LoRaWANs can be setup independent of network carriers. This enables it to be used for applications in remote contexts, such as agriculture, that do not have reliable wireless internet coverage.

4.44.2


HUMAN-MACHINE INTERFACES

This section provides a brief overview and forecast of human-machine interfaces.

Three emerging human-machine interfaces are then discussed in further detail: Natural

Language Interfaces, Conversational Systems, and Wearable User Interfaces.

A human-machine interface is a device or software that allows humans to operate

machines and technology. They are a broad class of technology, covering common

devices such as keyboard and mouse, and touch screen interfaces. Due to the ubiquitous

nature of machines and technology in our lives, most people will have some experience

with human-machine interfaces. As technology is becoming more sophisticated with

the growth of the Internet of Things and artificial intelligence, our interactions are

also changing. Human-machine interfaces are expanding to facilitate new methods of

interaction, such as voice and gesture.


FORECASTAs the range of applications, and devices that people interact with grow, novel methods

of human-machine interaction will increase in importance. It is believed that he next five

years will see significant innovation in human-machine interfaces (Cearley et al., 2016).

Billions of devices will implement zero-touch interfaces (Zimmermann et al., 2016).

Instead they will rely on other human senses such as sight and smell, as well as sensory

channels that extend beyond human senses, such as radar (Cearley et al., 2016).

Innovation of human-machine interfaces will be present across various industries, and

will be potentially transformative in industries relying on complex interactions between

humans and machines. With advanced human-machine interfaces, requirements for

computer literacy will diminish and smarter machines will make it easier for humans to

handle unfamiliar or complex situations (Brant & Austin, 2016). For instance, devices

and machines that have no visual interface, but that can carry out complex tasks based

on simple commands given by human workers.

As the number of connected devices grow, human-machine interfaces will also facilitate

ambient experiences. This is where machines and computerised devices can recognise

contextual information and then adapt experiences and actions as users passively move

through different locations or perform different tasks (Goertz & Reinhart, 2016).


NATURAL LANGUAGE INTERFACESNatural language interfaces facilitate interactions between humans and machines using

a range of input and output types, including sight, sound, touch, smell and radar. These

various interaction modalities are used to build a ‘conversation’ between the human and

machine to create an immersive, continuous and contextual experience (Cearley et al.,

2016). As technology advances in the areas of mobile computing, wearables and the

Internet of Things, zero-touch interactions between humans and machines will become

increasingly prevalent (Zimmermann et al., 2016).

APPLICATIONThe most differentiated applications facilitated by natural language interfaces will make

use of voice, ambient technology, biometrics, facial recognition, ‘sensored’ environments,

movements and gestures. Combining these technologies will allow for natural and

personalised interactions (Zimmermann et al., 2016). Interactions will be able to be

performed in multi-step sequences, and across multiple devices, systems and machines.

This will allow machines to understand user needs based on the tasks they are performing

as they move from place to place, and from data collected from previous tasks (Cearley

et al., 2016). The user will also be able to interact with the systems in meaningful ways

to instruct new actions (Brant & Austin, 2016).

These capabilities are demonstrated in the patent, mobile systems and methods of

supporting natural language human-machine interactions (Weider et al., 2016). The

functionality specified is for speech-based and non-speech-based interfaces for

telematics applications. Context, prior information, domain knowledge and user data

are used to create an environment for users to submit requests in a variety of domain

applications. The interface then responds to these requests in natural language. Extensive

profile information is stored for each user, thereby improving the capability to reliably

and accurately determine the context of the request and present expected results.


CONVERSATIONAL INTERFACESA conversational interface is a type of natural language interface which enables

interactions between a machine and a human to occur through spoken or written

language (Cearley et al., 2016). Well-established natural language interfaces include

digital assistants present on mobile devices and speaker products such as Google’s

Assistant, Amazon’s Alexa, Apple’s Siri and Microsoft’s Cortana.

APPLICATIONMainstream applications of conversational interfaces are still relatively primitive, allowing

people to ask basic questions or give basic commands (Brant & Austin, 2016) such

as make a phone call, set an alarm or schedule an appointment. Examples of this are

virtual assistants on smart phones and smart watches, and more recently on dedicated

hardware for the home, such as Amazon’s Echo speaker, and Google’s Home speaker.

These capabilities are also reflected in recent patents. For example, a patent for an

intelligent automated assistant is used to facilitate conversational natural language

interactions between a human and a computer on various platforms, including web and

mobile. These interactions are used to request various information or to request actions

be performed (Gruber et al., 2017). Similarly, technologies for conversational interfaces

for system control provides conversational control of a home automation system. Based

on spoken input, the computing device provides text response to the user and performs

the command given (Uppala et al., 2016). Interactions with conversational interfaces can

also be used to carry out very complex tasks. For example, collecting oral testimony from

crime witnesses which then results in the complex result of the creation of a suspect’s

image (Cearley et al., 2016).

In the last year, consumer applications of conversational interfaces have diversified

and spread to a variety of market segments (Crist, 2017). These include, but are not

limited to, televisions, fridges, robots, headphones, alarm clocks, automobiles (VW, Ford),

security systems, and lighting (Crist, 2017; Kastrenakes, 2017). A large part of this

diversification can be credited to Amazon’s decision to open its technology platform to

other vendors (Pierce, 2017). As the capabilities of conversational interfaces expand,

they will offer increasingly seamless and continuous interactions across different

products and devices in industry sectors as diverse as finance, retail, healthcare and

automotive (Bisht, 2016).


WEARABLE USER INTERFACESWearable user interfaces (WUIs) facilitate bidirectional communication between a

user and a computerised device. Communication can be passive or active. Passive

communication is where a wearable device picks up information from a user, technology

or context without direct input. This information can then be transmitted to other devices

or to the user directly. Active communication occurs where a user directly interacts with

the wearable device to issue commands or communicate with other technologies and

systems (Ghubril & Prentice, 2014).

APPLICATIONCurrent commercial applications of WUIs, such as smart watches and fitness trackers

are viewed to be marginally useful. The proliferation of Internet of Things devices will

lead to increasingly useful applications of WUIs, such as the ability to simultaneously

control a number of connected smart home devices (Ghubril & Prentice, 2014; Goertz

& Reinhart, 2016).

The most significant application of WUIs is in complex environments where users receive

and must be aware of simultaneous inputs from competing sources. In this use case,

WUIs are applied to reduce physical and cognitive workload. They can parse information

from peripheral devices and present it to the user, or they might be able to facilitate

simplified or automatic communication with peripheral devices. In military operations,

novel WUIs have been proposed to provide sensory enhancements for the identification

of critical features in an environment, and to aid navigation and communication (Laarni

et al., 2009). In the context of surgery, a patent for a wearable user interface for use with

ocular surgical console proposes a wearable device in communication with a surgical

console that simultaneously displays the surgical viewing area and peripheral data

relating to the surgery. This allows the surgeon to check settings and other information

without needing to pause the surgery to move their gaze (Yacono, 2014).

Other innovations are for directly controlling external computerised devices. A patent

for a method and apparatus for a gesture controlled interface for wearable devices

specifies a flexible device worn on the body comprising of sensors for detecting bio-

electrical signals and gestures. Triangulating inputs from each range of sensors allows

discriminations to be made between different gestures, subsequently allowing for the

control of a computerised device (Wagner, Langer, & Dahan, 2016).


0Very low impact

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2

3

4

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INNOVATION TRENDS3420

61 14

427

73

7

Hands free and non-verbal communication between human and machine facilitated by electronic wearable devices

Machines capable of understanding complex verbal commands and then carrying out relevant tasks

Interfaces that allow unskilled workers to operate complex equipment and carry out complex tasks

NATURAL LANGUAGE INTERFACES CONVERSATIONAL INTERFACES WEARABLE USER INTERFACES

EXPERT OPINION

Experts were surveyed and asked to rate the impact of human-machine interface technologies on a 5-point scale. On average, responses show that human-machine interfaces have potential impact for addressing skill shortages in rural workers, allowing unskilled workers to operate complex equipment. Interfaces that allow verbal communication between humans and machines were perceived to have higher impact than communication via electronic wearable devices.

Analysis of number of patents published by country identifies the USA as the main innovator of human-machine interfaces.

Analysis of number of patents published per year shows that innovation of natural language interfaces and conversational interfaces has occurred for several years, with peaks in 2014 and 2016. Innovation of wearable user interfaces has been less extensive, but more recent between 2014-2017.

HUMAN-MACHINE INTERFACES

Human-machine interfaces provide the means for humans and machines to communicate. With increasingly sophisticated technology and the growth of artificial intelligence, novel interfaces that accept input from various sensory channels will allow for unprecedented levels of communication between humans and machines.

3.63.5

3.3


SOLAR RETRANSMISSION

Aerospace technology plays an important role in precision agriculture. High resolution,

remote sensing imagery can be obtained at near real-time (e.g., planet.com) to provide

invaluable information about crop yield variability. This can be used to generate yield

maps in-season and to assist after-season management (Yang, Everitt, Du, Luo, &

Chanussot, 2013).

Beyond commercial services, there are even greater aspirations for how to leverage the

resources of space. Companies such as Planetary Resources (plantaryresources.com)

aim to unlock the solar system’s economy by mining asteroids for important elements.

The feasibility of such operations is highlighted by NASA’s Asteroid Redirect Mission

in 2020. This mission aims to retrieve a multi-ton boulder from a near-Earth asteroid,

and redirect it to a stable orbit closer to Earth where samples will be taken (NASA,

2015). Further, an area of sustained interest for aerospace technology is lunar-based

solar power. Various schemes have been proposed to capture solar energy in space and

beam it down to Earth for use (Bullis, 2009; Dorminy, 2017a, 2017b).

APPLICATIONWith potential impact and application for agriculture, a patent has recently been published

specifying a system for capturing and retransmitting solar energy for reinforcing

photosynthesis (Laine & Parrot, 2015). The technology comprises of a satellite with

two optical assemblies; the first for collecting sunlight, and the second for retransmitting

sunlight. The optical assembly for retransmission has a controllable orientation, meaning

that it could be positioned to retransmit sunlight to specific and modifiable locations on

Earth. Further, by transferring light through the optical assemblies only light in specified

frequency bands that are optimal for photosynthesis are retransmitted.

PREDICTIONThe aerospace industry is growing rapidly. In north America, the aerospace and defence

industry grew by 4.4% in 2016 to $365 billion. It is expected to reach $421 billion by

the end of 2021. Even faster growth is expected in the Asia-Pacific with the sector

growing 10.9% in 2016 to reach $272 billion. While much of this value is generated

through government contracts, the number of new private companies being created in

the commercial aerospace industry is increasing (Singh, 2014). Although the concepts

of lunar mining and solar retransmission are ideas ahead of their time, continued

commercial interest and innovation make them difficult to ignore.


GERMANY

0Very low impact

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2

3

4

Very high impact5

Real-time, high-resolution satellite imagery with interactive capabilities (e.g., zoom, overlays and movement)

Control and optimise sunlight exposure and growing conditions for crops

Control the frequency of sunlight for optimal photosynthesis (450nm to 660nm)

SOLAR RETRANSMISSION

Solar capture/retransmission satellite

Solar capture

Solar retransmission

The use of satellites and the imagery they capture is an important asset for precision agriculture. Although ahead of its time, the notion of using satellites to capture solar energy and retransmit it to Earth represents an iteration of a persistent interest in utilising space-based resources for applications on Earth. This proposed innovation sits in the context of active work in the areas of lunar-based mining and solar energy.

3.8

4.1

4.4

EXPERT OPINION

Survey responses suggest Experts were surveyed and asked to rate the impact of satellite imagery and solar retransmission technology on a 5-point scale. On average, responses demonstrate the value of satellite imagery for agricultural applications. Although perceived to have lesser impact, the capabilities of proposed solar retransmission satellites were of interest to survey participants. The ability to control and optimise sunlight exposure and growing conditions for crops was perceived to have particular impact.

INNOVATION TRENDS

The below diagram provides a visual representation of the proposed solar retransmission system in the patent, a system for capturing and retransmitting solar energy for reinforcing photosynthesis (Laine & Parrot, 2015).


PERSONAL ANALYTICS

This section presents and overview and forecast for personal analytics technology. Two

emerging personal tracking technologies are then discussed: Labour Tracking, and

Smart Contact Lenses.

The term personal analytics describes technologies and practices that are used to

monitor personal activities and behaviours. Personal analytics is also referred to as

personal informatics and quantified self (Lupton, 2016). One of the most obvious types

of personal analytics occurs via fitness trackers and smart watch devices, such as Fitbit

and Apple Watch. These products are usually positioned as technologies that facilitate

self-improvement and a healthier lifestyle. They propose to do this by monitoring and

providing feedback on variables such as calories burned, distance and speed travelled,

sedentary vs active time, and sleep quality. Users are then expected interpret and act on

this data in meaningful ways (Delgado, 2015; Lupton, 2016).

Personal analytics technology has received great interest for tracking workers in

hazardous environments such as factories and job sites. In such contexts, the application

of personal analytics technology is aimed at reducing accidents, improving worker

accountability, identifying worker fraud, and optimising work efficiency (Scism, 2016).

Personal analytics technologies are also viewed as important for tracking individuals

with special needs such as the elderly and hospital patients (Janwadkar, Bhavar, & Kolte,

2016).

FORECASTBig data is becoming an increasingly sought after and valuable resource for businesses.

Customer insights data can provide highly profitable forms of knowledge to inform

changes to business strategy and operations (Lupton, 2016). With personal analytics

technology, it is possible to generate similar insights about the behaviours of human

resources to create safer work environments and optimised operations (Ghubril

& Prentice, 2014; Zimmermann et al., 2016). Such capabilities are believed to have

application in a range of industries including healthcare, agriculture, and security and

policing (Lupton, 2016). Personal analytics technologies will offer particular benefit for

industries where workers perform jobs alone, in hazardous conditions, and where there

is the potential to harm others. In 2020, it is expected that 50% of employees in isolated

and hazardous working conditions, such as heavy machinery operation, will be monitored

by wearable personal analytics technology (Zimmermann et al., 2016).


LABOUR TRACKINGLabour tracking is the application of personal analytics technologies and practices to the

tracking and monitoring of human resources in workplace contexts. Methods for labour

tracking are diverse. They range from camera surveillance systems that monitor task

efficiency, to sensor enabled wearable devices that can track biometric inputs including

fatigue and heart rate (O’Connor, 2016). With the development of the Industrial Internet

of Things, innovation of labour tracking is being driven by the convergence of big data

analytics and low-cost sensing (Intel, 2015). Workers connected by intelligent sensor

devices and big data analytics are not only safer, but measures can be implemented to

increase worker and operations efficiency (O’Connor, 2016; Scism, 2016).

APPLICATIONLabour tracking has significant applications in industries that are hazardous (Scism,

2016), require workers to perform tasks in remote or isolated locations (Zimmermann

et al., 2016), and that require high standards of accountability (Scism, 2016). In such

contexts, labour tracking technologies include simple wearable devices, such as fitness

trackers that provide feedback to an individual and an organisation. On a more complex

level, labour tracking solutions can simultaneously measure biometrics as well as

environmental data. For instance, a “combination of skin temperature, raised heart rate,

and no movement patterns for several minutes could mean a person is suffering from

heat stress” (O’Connor, 2016).

Current labour tracking systems include Vandrico’s (vandrico.com) smart helmet

wearable device used to monitor miners. This device clips on to standard helmets and

provides 3D real-time location tracking, and hands-free communication to workers via

visual cues. A recent patent (Daniel, 2017), provides a system for managing teams and

assets based on geofencing principles. Members of a team are equipped with wireless

tracking devices that establish a virtual perimeter based on the location of individuals.

It is possible to specify maximum distance between members and send/receive alerts

when this is exceeded. Such technology has potential in critical environments, or in jobs

where specific relationships between workers and equipment is required. For example,

to maintain efficiency, or to reduce asset shrinkage.


SMART CONTACT LENSESSmart contact lenses are technology embedded contact lenses that are capable of

sensing various biometric signals, as well as providing enhanced functionality such as

augmented reality. Worn in the same manner as regular contact lenses, smart contact

lenses can offer unobtrusive monitoring of individuals, as well as provide significant

visual enhancements. Smart contact lenses represents a broader trend of nanosensor

wearables, that incorporate sensors that range from millimetre to nanometre scale

(Garcia-Martinez, 2016). At this scale, sensors can fit in discrete and unobtrusive

personal analytics devices.

APPLICATIONNotable applications of smart contact lenses come from two giants of the tech industry;

Google and Samsung. Google, in partnership with pharmaceutical company Novartis,

announced that it is working on a smart contact lens that measures blood-glucose levels

for people with diabetes (King, 2014). Samsung, on the other hand, has recently filed a

patent for a smart contact lens featuring a screen for use in augmented reality applications

(Samsung Electronics Co Ltd, 2016). It is argued that this will provides a more natural

augmented reality experience than larger head mounted displays (Chowdhry, 2016).

Samsung is not alone in developing display integrated smart contact lenses. A patent

specifying a smart contact lens with embedded display and image focusing system was

recently published (Shtukater & Raayonnova, 2017). This technology claims to monitor

a person’s head movement and gaze direction, which could be particularly useful for

evaluating worker vigilance and fatigue.

The capabilities of smart contact lenses emphasise the potential application of nano-

scale personal tracking devices. While smart contact lenses won’t be appropriate for all

people and industries, the development of discrete nanosensor tracking devices in other

form factors will have significant impact. Examples might be discrete and disposable

patches worn on the skin to detect fluctuations in temperature or environmental

contaminants. For this purpose, some of the most advanced nanosensors to date

have been ‘manufactured’ using synthetic biology and the modification of single-celled

organisms (Garcia-Martinez, 2016). Known as biosensors, these nano-scale biological

sensors are customisable to detect various types of chemical and biometric targets,

such as skin conductivity, pH, and temperature (Edwards et al., 2013).


0Very low impact

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CANA

DA

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12

4INNOVATION TRENDS

85 96

24-hour monitoring of worker health, including fatigue, stress and alertness

Communication of real-time personal health data to employers and medical practitioners

Track the location of workers in 3D space to protect them from hazards and reduce worker fraud

Track and monitor workers over time to optimise work schedule and increase productivity

EXPERT OPINION

Experts were surveyed and asked to rate the impact of personal analytics technologies on a 5-point scale. On average, personal analytics technologies are perceived to have greatest impact for the purpose of monitoring workers. The ability to track worker location to increase productivity and safety, and to monitor stress and fatigue were highly rated. Comparatively, communication of this data to health professionals was perceived to have less impact.

Analysis of patents published by country identifies the USA as the main innovator of labour tracking and smart contact lense technology. However, innovation has occured in Europe and Asia.

Analysis of number of patents published per year shows that innovation of labour tracking technology has steadily increased over the last several years. Innovation of smart contact lenses began in 2014 and has increased in 2016 and 2017.

LABOUR TRACKING SMART CONTACT LENSESPERSONAL ANALYTICS

The use of personal analytics technologies has gained increasing interest in work contexts to improve worker safety and increase operations efficiency. Advancements in wearable and nonosensor technology is driving innovation in unobtrusive personal analytics technology.

4

3.7

3.33.6


CONCLUSION

This report represents the first iteration in a revised horizon scanning scope. This revised

scope was initiated by the learnings gained from Horizon Scan 1, and targets the early

detection of emerging and transformative technologies that have potential impact for

Australian rural industries. Identification of technologies having impact internationally

and beyond the rural industries was highlighted as having particular value. To meet the

aims of the revised scope, our approach incorporates data mining, synthesis, industry

experts and visualisation across three interactive streams: discovery, evaluation, and

consolidation. The progression and interactivity of these streams has been designed

to detect emerging technologies across a broad range of domains, to understand the

value and potential of these technologies, and then extrapolate findings to present their

significance for Australian rural industries. The success of this approach is evident in the

outcomes that have been produced and discussed in this report.

In this first iteration, category and discrete technology types have been identified in

domains as diverse as information and communication technology, data analytics,

genomics, artificial intelligence, and renewable energy. Through our evaluation stream, a

sub-set of these technologies has been refined, leading to the 8 analysed and discussed

in this report. These 8 represent various types of innovation and potential impact for

Australian rural industries. For each of the technologies discussed in this report, we

have attempted to clearly communicate the essential elements of the technology,

current application areas, innovation trends, and an initial forecast for the continued

development and significance of the technologies. This process has principally been


achieved through data mining of patent data bases, and the synthesis of this and other

data sources. Further, the capabilities of each technology have been recontextualised

for Australian rural industries through engagement with industry experts, and in some

cases through early stage scenarios.

The extent of technologies identified, and their refinement and synthesis has provided

a demonstration of the utility of the discovery, evaluation and consolidation project

streams. As this report represents one iteration of each of these streams, our aim for

future reports is to build on the capabilities of each stream. Of particular importance

in this respect is the further extrapolation of our findings to clearly communicate the

transferability and subsequent impact of technologies for the Australian rural industries.

In the following reporting periods, we aim to implement multiple cycles of discovery,

evaluation and consolidation. In each successive iteration of this process, we will:

• Broaden our scanning scope to ensure coverage of technologies from a broad range

of domains.

• Expand upon the already identified technologies, and evaluate their impact for

Australian rural industries.

• Broaden data mining sources to strengthen the analysis of technology trends.

• Continue to monitor and develop knowledge of the technologies that have already

been identified to have potential impact for Australian rural industries.

• Continue engagement with QUT and RIRDC experts to gain insights into the specific

value and use cases of the identified technologies, and clearly communicate these

evaluations in infographic and scenario visualisations.


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APPENDIX: APPROACH AND PROJECT STREAMS

This project employs a concurrent phase, iterative methodology to:

1. scan and extract data from a variety of sources;

2. synthesise this information to identify potential trends and innovations for further

investigation;

3. and, communicate these issues through visualisations.

Implementation of each respective project phase, led by data mining, synthesis, and

visualisation, occurs across three interactive streams: Discovery; Evaluation; and,

Consolidation. These streams are continuous over the course of the project requiring

different inputs from each project phase. These inputs from each phase, and their

interactivity is represented in Figure 3.

FIGURE 3. PROJECT INPUTS AND STREAM INTERACTION

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DISCOVERYEVALUATIONCONSOLIDATION

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DISCOVERYDiscovery focused on scanning selected sources to extract relevant data and detect

signals of emerging and transformative technologies. Data sources were strategically

selected to ensure input from a diversity of domains within an international context.

In this implementation of the discovery stream, sources included select twitter users,

patent databases, industry and market reports, and technology news media. Scanning

of these sources has employed data mining and synthesis concurrently:

• Data mining focused on scanning the twitter feeds of thought leaders and technology

experts in a range of technology and industry domains. These were selected based

on the recommendations of QUT experts working in relevant technology domains.

• Synthesis focused on scanning a broad range of technology news publishers,

industry and market reports, and patent databases.

The confluence of data mining and synthesis outputs in the discovery stream resulted

in a list of technologies for further investigation in the subsequent evaluation and

elaboration streams.

EVALUATIONEvaluation focused on filtering the signals identified in the discovery stream. Its

implementation leveraged expertise from QUT, and RIRDC through the provided list

of innovative farmers, to assess the potential impact of a broad range of emerging

technologies. This process used Delphi style surveys which are deployed in successive

rounds.

The first round of surveys focused on evaluating a large volume of technologies identified

in the discovery stream. This was achieved through presenting a series of simple

statements that describe the functionality of a technology, and asking for a rating based

on its perceived impact. From these ratings, a short-list of technologies was compiled

for further investigation and ongoing monitoring. Successive survey rounds will present

a lower volume of questions but will require increasingly qualitative responses. It is

through this process that we will narrow the scope of technologies that will be included

on the watchlist and that will be monitored throughout future reporting periods. As the

evaluation stream progresses, we will develop an objective list of criteria to establish the

possible scale of impact of each transformative technology. These criteria will then be

re-introduced in successive discovery stages of the project to assist with filtering the

large volume of data.


CONSOLIDATIONConsolidation focused on monitoring technologies on the initial watchlist, and providing

a detailed analysis of their potential impact for Australian Rural Industries. This involved

a detailed analysis of the technologies’ potential impact for Australian rural industries

and developing a rationale for why they should be monitored. This elaboration stream

utilises both data mining and synthesis to receive input from a broad range of data, and

gain the requisite detail.

Data mining focuses on eliciting deeper analysis of the technologies identified and short-

listed during the discovery and evaluation streams. Directed by specific technologies

and related keywords, data mining targets social media feeds and patent databases. The

outcome of this stage provides assessment of candidate technologies based on metrics

such as trends over time, associated keywords and industries to identify the contexts in

which the technology is active and where it is receiving innovation, and location.

The data gained from this stage of data mining, and from the evaluation stream, feeds into

and directs synthesis. With this direction, synthesis can focus on specific applications and

implementations of the technology to better understand and communicate its potential

impact for Australian rural industries. Visualisation is then used to bring together the

inputs of synthesis and data mining, as well as inputs from the evaluation stream. This

includes the development of infographics to communicate the watchlist issues and

themes.

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