On  the  Horizon:  Smart  Agriculture  and  Big  Data  

Dr.  Rozita  Dara  Assistant  Professor  

School  of  computer  Science  University  of  Guelph  

Data  Collec9on  Data  Storage  

Data  Prepara9on  Data  Analysis    

&  Usage  

Data  Management    and  Privacy  Governance  Lab  

“an  all  encompassing  term  for  any    collec>on  of  data  sets  so  large  and    complex  that  it  becomes  

difficult  to    process  using  on  hand  data  management  tools  or  tradi>onal  data    

processing  applica>ons”      

 

Source:  hEps://ipp.cifs.cornell.edu/sites/ipp.cifs.cornell.edu/files/shared/Wiedmann%20IPP%20summit.pdf  

Four  Vs  of  Big  Data  

hEp://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-­‐Vs-­‐of-­‐big-­‐data.jpg  

hEps://csironewsblog.files.wordpress.com/2013/06/smart-­‐farming-­‐infographic_final-­‐png.jpg  

Agriculture  Data  Growth  

Supply  Chain  Complexity  

•  A  cup  of  Starbucks  coffee  can  depend  on  19  countries:  coffee,  milk,  sugar,  paper  cup,  and  other  factors.  

extensions to their drug product portfolio to layer relevant data sets together to provide transparency into the status of an individual animal at any point in time along its lifecycle. Regulatory bodies and policy makers will also soon realise the bene!ts of improved data collection, as some European countries now require farm-level data and documentation on antibiotic use (Maron et al., 2013). In turn, these digital technologies are ushering in a new era of best practices by enabling farmers and veterinarians to increase clinical treatment performance, and improve farm productivity and overall animal welfare at the same time. The IoAHT will create a new level of transparency and become a necessity in the study of translational medicine and for global research initiatives such as ‘One Health’. Digital technology, and the transformational societal bene!ts that it promises, have an entirely di"erent research and development process to drug technology and present a relatively new ‘space’ for pharmaceutical companies. This emerging ‘space’ presents new challenges, and not just for the pharmaceutical animal health company but rather for all

stakeholders dependent upon our societal food production capabilities.We carry a societal responsibility to use data in a positive way to maximise value to the production chain, while protecting the rights of individuals. With the development of the IoAHT and the intensi!cation of farming driving the rise of PLF, we now have a potential mechanism to support the collection of redacted data from individual animals to utilise big data for societal bene!t. The use of data in isolation does not ful!l its potential bene!ts: greater transparency of the food chain, improved traceability, as well as further improvements to animal health and welfare. This big data is essential when de!ning governmental policies, identifying new population trends and cultural shifts and allocating resources e#ciently; think of the value of human census data, which is personal data used for a societal bene!t. We have a moral responsibility to use big data e"ectively to oversee animal wellbeing, attempt to mitigate the forecast GAP index shortfall and to enhance food security as a result of a rapidly growing world population.

GLOBAL DEMAND FOR MEAT2005 vs 2050(in tonnes)

2005

2050

64M

106M

13M25M

100M

143M

82M

181M

62M

102M

BEEF MUTTON PORK POULTRY EGGS

Figure 2: Global demand for meat in 2050 (adapted from FAO, 2012; Gates Notes 2013)

Global  broiler  produc>on  market  stands  at  approximately  82  million  tones  of  meat.  

hEp://image.slidesharecdn.com/kpmgbigdatainhealthcare-­‐13454169797539-­‐phpapp01-­‐120819175826-­‐phpapp01/95/big-­‐data-­‐in-­‐healthcare-­‐6-­‐728.jpg?cb=1345399180  

Supply  Chain  Complexity  

•  A  cup  of  Starbucks  coffee  can  depend  on  19  countries:  coffee,  milk,  sugar,  paper  cup,  and  other  factors.  

hEp://www.fao.org/3/a-­‐ae930e/ae930e09.htm  

Data  Driven  Business  Model  

Case Study | Internet of Animal Health Things (IoAHT): Opportunities and Challenges

5

Data-Driven Business Model of Precision Livestock Farming (PLF)

PLF technologies can be incorporated into the Data-Driven Business Model (DDBM) framework, described by Brownlow et al. (2015), see Figure 3. In this section, we answer the six fundamental questions for PLF-DDBM innovation:

1. What do we want to achieve by using big data?2. What is the PLF-DDBM desired o!ering?3. What are the key data sources for PLF-DDBM?4. What are the key activities?5. What are the potential revenue streams?6. What are the challenges to us accomplishing our goal?

Figure 3: PLF-DDBM Innovation Blueprint

1 Target OutcomeUsing big data to improve the PLF process management and for targeted delivery of drugs to individual animals.

2 O!eringData: Continuous sensing of outputs (process responses) at appropriate scale and frequency, with data fed back to the process controller.

Information: A target value and trajectory for each process output such as growth rates, behaviour patterns.

Knowledge: Actuators and a predictive controller for the process inputs.

Data ErasureIssuesThird Party

Involvement Issues

Consent Quality Issues

User Access and Control Issues

Data Collection Issues

3 Data SourceInternal: Batch data collected by sensor devices such as herd/flock camera systems, automatic weighing devices, vocalisation monitors, cough monitors, electronic identification ear tags (EID) and pedometers.

External: Data obtained from collaboration with related parties, for example, feed manufacturers working with weight-monitoring PLF companies.

4 Key O!eringData acquisition: Capturing and recording multiple attributes of each animal such as age, pedigree, growth rates, etc.

Aggregation: Integrate data from di!erent devices.

Descriptive analytics: Temporal trend analysis, for example, monitor animals’ size and weight gain.

Predictive analytics: Predict the estimated real-time process output.

Prescriptive analytics: Enable interventions to ensure target trajectory is met.

5 Revenue ModelPotential revenue streams include usage fees, purchase of sensor devices, subscription fees. Data use may support other business core products providing market insight.

hEp://cambridgeservicealliance.eng.cam.ac.uk/Resources/Monthly%20Papers/2015JulyCaseStudyIoAHT_HQP.pdf  

 Enabling  Technology:  Decision  Support  System  

Decision  

Ac>on  

Report  

Weather  

Lab  data    

Farm  Data  

Maps  

Extract  Transform  Load  (ETL)    

Big  Data  Challenges  1.  Poor  integra>on  into  prac>>oners  workflow  

2.  Low  level  of  uptake  by  the  stakeholders  

3.  Cost  and  >me  required  to  develop  the  data  management  sysem  

4.  Inadequate  infrastructure  (e.g.  IT)  

5.  Interoperability:  technical  and  opera>onal  

6.  Poor  evalua>on  of  stakeholders’  needs  

7.  Lack  or  limited  commitment    of  the  subject  maEer  experts  

8.  Data  availability  and  quality    

9.  Analy>cal  challenges  

10.  Scalability    

Data  Prepara9on  Fa9gue  

Solu9ons:  Building  the  Team  

•  Subject  maEer  expert:  biologist,  engineer,  health  specialist,  Vet,  someone  who  knows  the  problem  domain  

•  Data  scien>sts:  sta>s>cian,  data  mining  expert  

•  Privacy  specialist:  engineer,  requirement  engineer,  policy  analyst,  lawyer  

•  Stakeholders:  end  user  (e.g.  farmer),  policy  maker,  consumer,    general  public  

 •  Story  teller:  end  user,  subject  maEer  expert,  marketer  

Thank  you!