On the Horizon: Smart Agriculture and Big Data. Dr. Rozita Dara Assistant Professor School of computer Science University of Guelph

Transcription

1 Data Management and Privacy Governance Lab Data Collec9on Data Prepara9on Data Storage Data Analysis & Usage On the Horizon: Smart Agriculture and Big Data Dr. Rozita Dara Assistant Professor School of computer Science University of Guelph

2 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

3 Four Vs of Big Data hep:// Vs- of- big- data.jpg

4 Agriculture Data Growth heps:// csironewsblog.files.wordpress.c om/2013/06/smart- farming- infographic_final- png.jpg

5 Supply Chain Complexity A cup of Starbucks coffee can depend on 19 countries: coffee, milk, sugar, paper cup, and other factors.

6 emerging space presents new challenges, and not just for the pharmaceutical animal health company but rather for all GAP index shortfall and to enhance food security as a result of a rapidly growing world population. GLOBAL DEMAND FOR MEAT 2005 vs 2050 (in tonnes) M 143M 64M 106M 100M 82M 62M 102M 13M 25M 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.

7 hep://image.slidesharecdn.com/kpmgbigdatainhealthcare phpapp phpapp01/95/big- data- in- healthcare jpg?cb=

8 Supply Chain Complexity A cup of Starbucks coffee can depend on 19 countries: coffee, milk, sugar, paper cup, and other factors. hep:// ae930e/ae930e09.htm

9 5. What are the potential revenue streams? 6. What are the challenges to us accomplishing our goal? Data Driven Business Model Third Party Involvement Issues Data Erasure Issues 1 Target Outcome Using big data to improve the PLF process management and for targeted delivery of drugs to individual animals. Data Collection Issues 4 Key Offering Data acquisition: Capturing and recording multiple attributes of each animal such as age, pedigree, growth rates, etc. Aggregation: Integrate data from different 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 Model Potential revenue streams include usage fees, purchase of sensor devices, subscription fees. Data use may support other business core products providing market insight. 2 Offering Data: 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. Consent Quality Issues 3 Data Source Internal: 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. User Access and Control Issues Figure 3: PLF-DDBM Innovation Blueprint hep://cambridgeservicealliance.eng.cam.ac.uk/resources/monthly%20papers/2015julycasestudyioaht_hqp.pdf

10 Enabling Technology: Decision Support System Weather Decision Lab data Farm Data Extract Transform Load (ETL) Ac>on Maps Report

11 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

12 Data Prepara9on Fa9gue

13 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

14 Thank you!