kim bryceson the university of queensland australia technology food... · wireless sensor network...

2016 Seminar Series - Nov 17 2016

Technology and Food Security -

Mixing and Matching needs Kim Bryceson

The University of Queensland Australia


The Question

The Internet of Things, Big Data, Drone technology, Smart Agriculture, the “eLandscape” of business, Cold Chain Logistics, - how do these “Buzz” phrases that encompass a vast array of different technologies have anything to do with Food Security? Underpin the capability of the world to deliver a Food Secure future


This Talk

Emerging technologies that are currently being used and are likely to be used in the future for investigating and developing solutions for managing Food INSecurity

Thus leading to the goal of Food Security


Food Security vs Food INsecurity

Food security: exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food • Food is available • Food is affordable • Food Is utilized

Food INsecurity: is about hunger – that is, only having irregular access to safe, nutritionally adequate, culturally acceptable food from non-emergency sources. • What factors contribute to food

insecurity? • How many people do not have

enough to eat? (ie how many are food insecure?)

• Where do they live? • How could one describe these

countries? (think about their economic, social, political and environmental conditions)


What contributes to Food INsecurity?

Trade

Population and Urbanisation

Disasters and Conflicts

The Need for Gender Equity

Water, Environment and Climate Change

Poor Health

Poverty


Addressing these issues?

Smart Production - Improving Food

Production Sustainably

Agrifood Supply Chain/Network

Management and Capability

Development Distribution of

Food resources (Smart Logistics)

Health (Nutrition - link to Smart Agriculture)

Economic Growth Recognising the Role of Women Food Aid

Technology, Multidisciplinary, Long term Education / Capacity Development


Food Production Challenges

A soaring global population – We need to feed more with less resources

– Food production must become smarter

– Production must be sustainable = Education, Knowledge , Young people

Agriculture is difficult to get young people into – Perceived as labour intensive and non-academic and no $

Technology is pervasive – Miniaturisation of electronics & automation are key drivers of innovation

– Can we harness to engage and develop the new skills needed in Ag?


Implications & Needs

• Need smart science to address production and associated sustainability issues

• Need specialist skills to feed the world

• Must use new technologies


Specialist Skills Growing things

– Production (commodity crops, livestock, intensive & organic production )

– Animal Science (physiology, nutrition, reproduction, husbandry etc)

– Animal Health & Welfare

– Vet Science

– Plant Science (crop/pasture physiology, nutrition, genetics etc)

– Soils (chemistry, physics & soils management)

– Chemical management (fertilisers, herbicides, pesticides)

– Managing spatial variability (of the land, pest & weed management, water management)

– Managing sustainability (carbon footprint)

– Technology (machinery, Precision Agriculture - RFID, GPS, IoT etc )

Agribusiness

– Industry Supply Chains (grain, meat, dairy, nuts, fruit & veg, honey, cotton, wool, beverages etc)

– Processing (functional & specialty ingredients)

– Manufacturing (product development)

– Distribution & Logistics (transport & refrigeration)

– Marketing & Branding (market, consumer research)

– Retail (food packaging, sales, food safety & traceability)

– Finance & Risk Management

– Exporting

– Commodity Trading

– Biotechnology

– Animal feed (pet food and biomaterials)

Biofuels

– Ethanol (derived from food crops and biomass)

– Biodiesel (multiple feedstocks)

– Clean technologies (commercialization of waste streams and by-products)

HEALTH


Precision Agriculture: we are talking about?

Tailoring soil & crop management to match conditions at every location in a field from year to year – on a whole farm basis

“Size” of location can vary – e.g. 50cmc, 1ha, 10ha

Location is geographically referenced using GPS

Managing Spatial variability


In terms of our Buzz words/phrases?

Internet of Things

Drone Technology (robotics)

Big Data

eLandscape


Internet of Things

IoT = Internet of Things - a network of physical objects that contain embedded technology to communicate, sense &/or interact with their internal states or the external environment + the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction

Leads to “Big Data” – i.e. lots of it


Wireless Sensor Network (WSN) Mesh typology, flexible, self configuring & healing (fault tolerant) Robust and low maintenance Multiple sensors 6+ Multiple communication protocols • Point to point, star, mesh. • >12 radio interfaces (i.e. Wi-Fi, 3G, LoRA, ZigBee)

Nodes solar powered + 24hrs backup Wi-Fi connected to Eduroam

GRAND TOTAL in the network = 60 nodes, 4 Meshliums, 8 cameras

All OK Problem develops Self healing

Nodes

Meshlium (router for communication)


Libelium Nodes and Sensors

Smart Agriculture

Smart Water

Smart Environment

Smart Security

http://www.libelium.com/products/plug-sense/models/


Types of Sensors Installed

Libelium Waspmote Node Model

Sensors possible

Smart Environment Carbon Monoxide (CO), Carbon Dioxide (CO2), Oxygen (O2), Methane (CH4), Hydrogen (H2), Ammonia (NH3), Hydrogen Sulfide (H2S), Nitrogen Dioxide (NO2), Ozone (O3), Hydrocarbons (VOC), Isobutane, Ethanol, Toluene, Temperature, Humidity, Pressure atmospheric, dust particles, Luminosity.

Smart Agriculture Soil moisture, Soil temperature, Leaf wetness, Solar radiation, Atmospheric pressure, Stem diameter, Anemometer, Wind vane, Pluviometer, Ambient temperature, Humidity

Smart Water Temperature, pH, dissolved oxygen, oxidation-reduction potential, conductivity (salinity), turbidity, dissolved ions (Calcium (Ca2+), Fluoride (F-), Fluoroborate (BF4

-), Nitrate (NO3-), Bromide (Br-), Chloride (Cl-), Cupric

(Cu2+), Iodide (I-), Silver (Ag+)

Smart Security Water presence, Liquid level, Liquid flow, Temperature, Humidity, Luminosity


Sensors


Technology in place on Campus ARSL

Weather Station atop SAFS

Smart Cities Node on Campus

Multiple sensors

Smart Ag Node


Agriculture Node Build Modular Components

Finished Ag. Sensor Node Electronic Components

Instrumentation & Materials


100m x100m mesh

Support Structure for in Paddock Deployment Support Post & Cable Harness


100m x100m mesh

Paddock Installation (Darbalara Farm)


The TITANIC

In the Water

Navas 2016


Wireless Self-Powered Camera

Navas 2016

• WI-FI & 3G connectivity

• Self powered

• Night vision capability (IR Leds)

• Motion detection

• Equine birthing


Smart Campus + TEL TEL PROJECT Smart Campus Initiative (IoT)


Interactive GUI (DGLux5)

QR Code:

Scan to access site


Waste Water PBL Lake Galletly, Mac’s Pond, Lake Lenore are all part of the Waste Water Management system at UQGatton Campus


So What?

Ok – we have large amounts of biophysical data coming in – what else do we need – and for what?

• Want to use it for lots of things

Pasture monitoring & management

Animal monitoring & management

Crop monitoring & management

Education

More data ! - eg Aerial data

Drones


Back to the Future - New Technologies in Ag

Remote Sensing (since early 1970s) – Gathering data at a distance across large areas for spatial variability

monitoring

– Eg Satellite or Airborne

Chequered history in Ag with 2 main issues 1. Data

• Cost of acquisition & processing • Revisit Frequency • Resolution • Requires cloud/haze free environment • Computing/processing grunt available

2. Lack of skills available in the Agricultural sector


Industry - Last 5 years Data – Satellite data can be obtained free for whole world through NASA and

other Government databases

– Still has issues of resolution and revisit frequency for agricultural purposes

Exponential growth in the miniaturisation of electronic equipment

• Driven growth of small drones – multirotor and fixed wing


Drones

Reason for use – Cheap platform to carry high res sensors

– Collect data on spatial variability (SV) Optimising Production Efficiency & Quality

Minimising Risk & Environmental Impact

= SMART FOOD PRODUCTION

= SMART ENVIRONMENTAL MANAGEMENT

= SMART SKILLS DEVELOPMENT Multiple skills and fun!

Drones at UQ

– 2016 UQ’s fleet consists of 5 DJI Phantoms + 4 bespoke Quads + 3 bespoke Hexicopters + 10 MiniAg drones (Ekka)

10 mins to learn to fly ** Vegetables Australia Magazine Article March/April 2015 http://www.ausveg.com.au/publications/VA/VA-MarApr2015.pdf

SV at ground level

SV from the Air


Navas, 2015

UQGatton Drones

Drone Design Projects 2013-2016 TestDrone, BugDrone, NetDrone, WeedDrone, WildDrone, WhaleDrone, RFIDDrone, Mini-AgDrones with NoIR camera

2013

2014/15

2014

2015

2015/2016

2015/16

2013-2016


Design & Build = Active Learning

Students from a range of degrees – BSc, BEng, BAgSci, BAgriBus

– Design, build and use a small to medium drone

Learn general design & build principles including testing

Learn to fly

Undertake project

Write Report – develop Recipe/Poster/Paper


TestDrone (2014)

Bryceson 2014


UQGatton Farm from a drone

Paddocks &

Maize Visual

Infrared

Pivot irrigated Lucerne Visual

X X

X

Infrared

*

X

X

X

*


WeedDrone

Bryceson, 2014


Beneficial BugDrone

Rowena Dione Photography 2015


NetDrone

Photo: Sotiris Ioannou UQ, 2015


UQMiniAg Drone

Navas, 2015


Robotics

AgBot – QUT

Harvesting

Dairy

Other

https://www.youtube.com/watch?v=15tovWSnJe0





https://www.youtube.com/watch?v=EElFsj_9JWM

https://www.youtube.com/watch?v=cFNfT-KO-h0

https://www.youtube.com/watch?v=3RN378Vuwts


Sensors – What are they recording?

Why is Grass green, or a Tomato red?

Tomatoes are red because when ripe, they contain a carotenoid known as "Lycopene"

Lycopene is a bright red carotenoid pigment which absorbs most of the visible light spectrum, and being red in colour, Lycopene reflects mainly red back to the viewer, thus a ripe tomato appears to be Red

Grass???


Spectral Reflectance characteristics

Use ENVI software to look at the spectral properties of the ground as recorded

Spectral reflectance characteristics due to:

• Pigmentation • Moisture content • Cellular structure • Mineral & moisture content of soils • Sedimentation levels in water

Raspberry Pi NOIR Sensor on MiniAg Drone


Multispectral Camera

Based on Raspberry Pi & 4 NoIR multiplexed cameras.

4 selectable wavelength bands (research quality filters)


MSS Data displayed on screen

IR Red Green

MSS


Addressing Food Insecurity issues?

Optimising Production Efficiency (IoT and NetDrone)

Optimising Quality (IoT and MiniAg Drone)

Minimising Risk (IoT and WeedDrone)

Minimising Environmental Impact (IoT and BugDrone)

Education (IoT + Drones = Active Learning)


Big Data

Many definitions:

– Big data is new and “gi-normous” and scary – very, very scary.

– Big data is just another name for the same old data marketers have always used, and it’s not all that big, and it’s something we should be embracing, not fearing.

– Big data is as powerful as a tsunami, but it’s a deluge that can be controlled - in a positive way, to provide business insights and value.

Big data is a collection of data from traditional and digital sources inside and outside the organisationthat represents a source for ongoing discovery and analysis.

http://harvardmag.com/pdf/2014/03-pdfs/0314-30.pdf


Key Issues for Big data Big Data success is not about implementing one piece of technology (eg Hadoop)

– Requires putting together an assembly line of technologies, people & processes

You need to capture data, store data, clean data, query data, analyze data, visualize data. – Some of this will be done by products, and some of it will be done by

humans. Everything needs to be integrated seamlessly

http://www.sas.com/en_us/insights/big-data/hadoop.html


Elandscape of Business


‘E’ Agribusiness Supply Chains

Supply Chain Management - originally envisaged as ‘the elimination of barriers between trading partners’


‘E’ Supply Chains

Companies with a network of suppliers, partners, and customers connected electronically

Because?

– Need a fast, efficient way to do business, disseminate information and enable two-way communications

Involve

– the complete integration of internal ERP & other business systems

– Collaboration with internal and external supply partners


‘E’Supply Chains & Food Security/Insecurity?

Logistics and Delivery

Food Tracking & Traceability - different but same concepts

All about ‘E’ connectivity and information flow

EReadiness: - the extent to which a country’s business environment is conducive to Internet-based commercial opportunities • Connectivity • Business Environment • Ecommerce & Consumer adoption • Legal & Regulatory Environment • Supporting e-Services • Social & Cultural Infrastructure


Food Tracking & Traceability

We are what we eat!!!

Coffee


Food Tracking/Traceability

We Are What We Eat

Traceability: The ability to trace and follow a food, feed, food producing animal or substance intended to be, or expected to be, incorporated into a food or feed through all stages of production, processing and distribution

Traceability systems are record-keeping systems that act as a tool for making information available either within an organization or between organizations

Records have to be authentic, reliable, integral (or whole), useful (and useable)

Product Traceability Systems - Recall


Two Important Traceability Systems

The Barcode system

The RFID System


Logistics

Distribution of Food – Specialised Services – Traceability affects supply chain efficiency, product safety and security,

managing deep tier risks, on-time delivery performance, troubleshooting customer issues, controlling costs, and regulatory compliance

USA

Europe - Europool

http://www.europoolsystem.com/en

http://www.igps.net/


Global Food Security & ‘E’ Techs?

Food Security Analysis Data Visualisation

http://www.agri-outlook.org/


Food Security Analysis

Decision makers at country, regional and global levels need reliable and timely information – on the incidence and causes of food insecurity, malnutrition and vulnerability

– for improved policy and program formulation aimed at reducing poverty and hunger

Problem: – A lack of common definitions for classifying various food security situations in terms

of varying severity and implications for action

– Problematic for several reasons: • What data has been used

• The way a situation is classified determines not only the type of response, but also the source of funding, scale, planning timeframe, and organizational roles of different stakeholders.

• Without commonly accepted standards for classifying the nature and severity of food security situations, the design and targeting of interventions can be open to personal, government, agency, and donor biases.


Analysis Frameworks

There are 3 examples of Analysis Frameworks that have been developed over time and show this lack of commonality: – VAM – Vulnerability Analysis Mapping – from the World Food Programme (WFP) –

the largest humanitarian agency fighting hunger worldwide

– FIVIMS – Food Insecurity and Vulnerability Mapping – from FAO - the Food and Agriculture Organization of the United Nations (2015 - No Longer Operational)

– FSF - Food Security Framework – from USAID - United States Agency for International Development


The World Food Programme (WFP) & VAM

WFP's food security analysis work is commonly known as VAM (Vulnerability Analysis and Mapping)

VAM is based around knowing the answers to the following questions: – Who is food insecure or vulnerable? – How many are they? – Where do they live? – Why are they food insecure or vulnerable? – How is the situation likely to evolve and what are the risks threatening them? – What should be done to save their lives and livelihoods?

And helps to: – Identify the most appropriate type and scale of intervention, whether food distributions, school feeding, etc – Identify support to re-establish livelihoods or more innovative interventions such as cash or voucher programmes; – Identify the most food insecure people to ensure the most effective targeting; – Ensure the most efficient use of humanitarian resources, by allocating funding according to needs

http://www.wfp.org/

http://www.wfp.org/food-security

http://www.wfp.org/videos


FAO & FIVIMS

The FAO’s FIVIMS Initiative (Food Insecurity and Vulnerability Mapping System) promoted cross-sectoral analysis. It aimed at: – Raising awareness about food security issues – Improving the quality of food security related data and analysis – Facilitating the integration of complimentary information – Promoting a better understanding of User’s needs and a better use of information – Improving access to information through networking and sharing

The FIVIMS conceptual framework is a cross-sectoral food security analysis helps strengthen the understanding of why people are food insecure, malnourished or hungry

There are four key dimensions associated with the analysis: – Food availability – Food Access – Supply stability – Resource Utilisation

http://www.fao.org/

http://www.fao.org/hunger/en/


FAO/FIVMS


USAID

The USAID food security framework highlights the three dimensions of Food Availability, Food Access, and Resource Utilization and the nature of their relationship to one another, and what determines these relationships

– Food availability is a combination of domestic food stocks, commercial food imports,

food aid, and domestic food production, & the underlying determinants of each of these factors

– Food access is influenced by the aggregate availability of food through availabilities impact on supplies in the market and, therefore, on market prices

– Resource Utilisation - food access also is a function of the physical environment, social environment and policy environment which determine how effectively households are able to utilize their resources to meet their food security objectives. Drastic changes in these conditions, such as during periods of drought or social conflict, may seriously disrupt production strategies and threaten the food access of affected households.

http://www.usaid.gov/

http://pdf.usaid.gov/pdf_docs/Pnacg170.pdf


Food Security Monitoring

Monitoring - the systematic collection and analysis of information to provide information for decision-making to answer the following questions:

– How much food is the world producing? – What is happening to world food prices? – What is the impact of El Niño and La Niña weather events on food production – How is the monsoon progressing in East Asia? – Will there be a drought in southern Africa this year? – What is the impact of floods and other weather hazards on food production? – What are the food security implications of civil war, economic crises or other man-made

disasters? – Which countries are the most food-insecure? – Where are food interventions most needed? – Where are cereal surpluses available for local purchases or triangular transactions?


Food Security Monitoring

A system or series of systems is needed that can – Monitor food supply and demand in all countries of the world on a continuous basis

– Compile and analyse information on global production, stocks, trade and food aid

– Monitor export prices and developments on main grain exchanges

– Provide up-to-date reports and information to the international community through regular publications, special reports, e-mail and its web pages on the Internet

– Answer specific requests for information, from governments, NGOs, research institutions and individuals

– Develop new approaches and technologies for early warning and make these available to national and regional early warning systems

– Cultivate and maintain a commitment to global food information-sharing between governments, NGOs, other UN agencies, research institutions, the international press and private individuals

https://www.google.com.au/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&ved=0ahUKEwixqPzg0K7QAhWMy7wKHccfANUQjRwIBw&url=http://ssa.foodsecurityportal.org/&psig=AFQjCNG2SDzqkNTqXzItvEaIP4xyurgR-Q&ust=1479432197063863


GIEWS

By far the most well known and well developed system is GIEWS -FAO’s Global Information and Early Warning System on Food and

Agriculture

– Created in 1975, the FAO’s Global Information and Early Warning System on Food and

Agriculture (GIEWS) has been providing policymakers and policy-analysts with the most up-to-date and accurate information available on all aspects of food supply and demand

• Provides regular bulletins on food crop production and markets at the global level and situation reports on a regional and country-by-country basis and warns of imminent food crises, so that timely interventions can be planned and suffering avoided

http://www.fao.org/giews/en/

http://www.fao.org/giews/en/

http://www.fao.org/giews/countrybrief/




GIEWS

The Service is responsible for planning and backstopping FAO-supported Regional and National Early Warning and Food Information Systems

In case of impeding food emergencies, the System dispatches rapid Crop and Food Supply Assessment Missions (CFSAM) to the afflicted regions, often jointly with the World Food Programme (WFP).


Food Security Mapping

Mapping - Knowing the spatial distribution of vulnerability to

food insecurity as well as understanding the determining factors is a prerequisite in the situation analysis needed for designing and planning responses to food insecurity

– Need to create maps that help governments, donors and

development agencies to understand the underlying causes of poverty

• Dynamic vulnerability in food insecurity maps


THE END

kim bryceson the university of queensland australia technology food... · wireless sensor network...

Documents