Big Data and Precision Dairy Farming
•Download as PPTX, PDF•
1 like•704 views
this presentation was prepared for a minisymposium on the occasion of PhD defence of Niels Rutten June 14 2017 at Wageningen University, with the thesis entitled “The utility of sensor technology to support reproductive management on dairy farms”. The public defence of his thesis was a good reason to share knowledge about current sensor research in the dairy farming industry
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Big Data and Precision Dairy Farming
Similar to Big Data and Precision Dairy Farming (20)
More from Claudia Kamphuis
More from Claudia Kamphuis (11)
Recently uploaded
Recently uploaded (20)
Big Data and Precision Dairy Farming
- 1. Precision Dairy Farming and Big Data Claudia Kamphuis WUR Animal Breeding and Genomics
- 2. Road map Big Data Precision Dairy Farming Big Data = Precision Dairy Farming? Big Data projects related to dairying @WUR 2
- 3. Big Data
- 4. Big Data 1.79 billion 317 million monthly active users
- 5. Big Data
- 6. Big Data 6
- 7. Big Data: about V’s and one A 7 A Volume VelocityVariety John Mashley, 1990s
- 8. Big Data: about V’s and one A 8 A Volume Velocity Variety VeracityVariability Value Visualisation
- 9. Big Data: about V’s and one A 9 A Volume Velocity Variety Veracity Variability ValueVisualisation Virality Viscosity Validity ...
- 10. Big Data: about V’s and one A 10 Analytics Volume Velocity Variety Veracity Variability ValueVisualisation Virality Viscosity Validity ...
- 11. Precision Dairy Farming (PDF) Sensor or automation technologies to Reduce human labour Support (daily) management Improve farm profitability & sustainability But also... Monitor parameters related to health/fertility of individual cows Automatic detection of events (e.g. estrus and mastitis) 11
- 12. Big Data = PDF? 12 Analytics Volume Velocity Variety VeracityVariability Value Visualisation
- 13. Big Data = PDF? 13 Analytics Volume Velocity Variety VeracityVariability Value Visualisation
- 14. Big Data = PDF? 14 Analytics Volume Velocity Variety VeracityVariability Value Visualisation
- 15. Big Data = PDF? 15 Analytics Volume Velocity Variety Veracity Variability Visualise Value
- 16. Big Data ≠ PDF? 16 Analytics Volume Velocity Variety Veracity Variability Visualise Value
- 17. Data many cows/farms for many years Sources data reside and originate outside farm fence Interaction across farms and through chain Complex interaction across several factors Aggregate data from many sources Data few cows (herd)/ 1 farm ....................................... Sources of data reside and originate on cow / on-farm........ Tools for farm management ......... Research is focused on one factor ..... Data from a few sources, or from same tech provider ................. Big Data ≠ PDF 17 PDFBig Data
- 18. Big (Dairy) Data projects @WUR 18 Estimating cow individual feed intake Smart animal breeding Predict longevity Gentore Tools for resilience & efficiency
- 19. Estimating cow individual feed intake More efficient food production required to feed the world More efficient cows needed Improve feed efficiency
- 20. Estimating cow individual feed intake More efficient food production required to feed the world More efficient cows needed Improve feed efficiency
- 21. Estimating cow individual feed intake 21
- 22. Big Data components 15 years data 7 farms 1850 cows Variety of sources Machine learning 22 Analytics Volume Velocity Variety Veracity Variability Visualise Value
- 23. STW-Breed4Food Partnership Programme Smart animal breeding: predict longevity Survival to second lactation + one week complex/summary trait from several factors economically important early prediction of survival allows for improved breeding and culling management decisions 23
- 24. Big Data components 24 Machine learning Several sources genomic, phenotypic, environmental, sensor Large number of animals Analytics Volume Velocity Variety VeracityVariability Visualise Value
- 25. Horizon 2020 Gentore: tools for resilience & efficiency Develop innovative tools to optimise resilience and efficiency in widely varying environments beef, milk, and mixed environments management tools for on-farm assessment for animal resilience to allow improved breeding and culling decisions Develop breeding strategies Provide policy support by using tools to compare future incentive/risk scenarios 25
- 26. Big Data components Multidisciplinary scientific teams Chain level - breeding organisations - farm tech companies - advisory services (e.g vet) - farm sectors Data base including >1 million genotypes 26 Analytics Volume Velocity Variety Veracity Variability Visualise Value
- 27. Take home messages around Big Data Big Data is more than just a lot of data (volume) Big Data is a rookie within livestock sector Not everybody is doing it yet PDF is a data source for Big Data Added value of Big Data is yet to be proven 27
Editor's Notes
- So, Big Data. If you have not heard about this by now, you must have been on a long holiday without wifi. You all must be familiar with the famous stories of facebook, google and twitter all collecting and analysing huge volumes of data, turning it into information and into profit. Or the use of big data by retail, keeping records on things people purchase and use this information for logistics, or to decide which products to discount, and even where to put products in the store. Banks use bank account data to create products that better suit their clients UPS is making reference to using Big Data approach to optimize their routing which saved them millions on fuel costs on an annual basis All in all, Big Data is known via these large companies, and all state that BigData made them rich. Big Data stands for combining large amounts of data in smart ways to improve or optimise management and success is guaranteed. And because everybody seems to be working with big data, it’s definately a buzzword.
- So, Big Data. If you have not heard about this by now, you must have been on a long holiday without wifi. You all must be familiar with the famous stories of facebook, google and twitter all collecting and analysing huge volumes of data, turning it into information and into profit. Or the use of big data by retail, keeping records on things people purchase and use this information for logistics, or to decide which products to discount, and even where to put products in the store. Banks use bank account data to create products that better suit their clients UPS is making reference to using Big Data approach to optimize their routing which saved them millions on fuel costs on an annual basis All in all, Big Data is known via these large companies, and all state that BigData made them rich. Big Data stands for combining large amounts of data in smart ways to improve or optimise management and success is guaranteed. And because everybody seems to be working with big data, it’s definately a buzzword.
- When you place Big Data in an agricultural setting, I think that this picture summarizes Big Data. Or I should be more precise, the driver of this tractor symbolises the concept of Big Data. Why? Well, the driver is invisible, similar as Big Data is invisible, and thus both are a bit mysterious... More importantly, I think that what the driver is doing, is similar to what Big Data is doing. The driver combines information from several sources, all having different formats, some come in continuously, others less frequent but the driver is using all this information near real-time to adjust his route, or the amount of fertilizer, or whatever he’s doing on this tractor. He’s adjusting or is making decisions based on the realtime information coming in. As in many sectors, Big Data is of increasing interest livestock, because also here the amount of data that is being generated is continuously growing, partly because of the increasing amounts of sensor technologies. We have sensors monitoring how active a cow is, where she is, how much she’s eating, how much milk she’s producing and with what composition, we have the information around the DNA of cows. We even have information of parcels, how much grass is growing, how are they fertilized etc etc....all this increasing volumes of data are food for thoughts, and makes Big Data We also have increasing amounts of data of parcels, how much product they are generating via drone images.
- When you place Big Data in an agricultural setting, I think that this picture summarizes Big Data. Or I should be more precise, the driver of this tractor symbolises the concept of Big Data. Why? Well, the driver is invisible, similar as Big Data is invisible, and thus both are a bit mysterious... More importantly, I think that what the driver is doing, is similar to what Big Data is doing. The driver combines information from several sources, all having different formats, some come in continuously, others less frequent but the driver is using all this information near real-time to adjust his route, or the amount of fertilizer, or whatever he’s doing on this tractor. He’s adjusting or is making decisions based on the realtime information coming in. As in many sectors, Big Data is of increasing interest livestock, because also here the amount of data that is being generated is continuously growing, partly because of the increasing amounts of sensor technologies. We have sensors monitoring how active a cow is, where she is, how much she’s eating, how much milk she’s producing and with what composition, we have the information around the DNA of cows. We even have information of parcels, how much grass is growing, how are they fertilized etc etc....all this increasing volumes of data are food for thoughts, and makes Big Data We also have increasing amounts of data of parcels, how much product they are generating via drone images.
- But, how is Big Data defined. What is it exactly? The tern Big Data is probably introduced by John Mashely in the early 1990s, where he used the term to warn the high-tech community of that time that challenges were about to occur with the continuous advances in computer storage, processing power, and developments in the generation of data. It took some years before the three characteristics got associated with it to form today’s mainstream definition. These characteristics are the first, and most commonly known, three Vs. Firstly, Volume. I deliberately increased the size of this circle because Volume is likely the first thing people think about when talking about Big Data. It’s BIG. It’s the characteristic that is often emphasized in the media, and it’s also the characteristic to impress people the easiest. At the same time, the threshold of what the volume should be to be called Big is unknown, and the threshold is subjective and dependent on the industry and application. It may be anything whose size exceeds the ability of typical software used to capture, store, manage, and analyse or any attribute challenging the constraints of a system capability or business needs. Others call it Big when data sets are so large or complex that traditional data processing applications are inadequate. And to add a bit of complexity, data we consider Big today may not be considered Big tomorrow due to the continuous advances in processing, storage, and other system capabilities. The other two characteristics are Velocity referring to the Data component of Big Data. It links to how frequently data are generated, the increasing speed of data arrival and processing, and the ability to respond to events as they occur and Variety also referring to the Data component of Big Data. It links to the variety of incompatible formats, non-aligned structures, and inconsistent semantics and this variety in data sources have been mentioned as the greatest barrier to effective data analysis.
- Over the years more Vs were added to define Big Data. Veracity: refering to the quality of data, the need to trust the data Variability: variance in meaning in sentiment analysis or the inconsistency of data Visualization: creation of complex graphs Value: the value of relying on data-driven analysis for decision - making
- And even more without being explained at all.....and of course we can expect some new ones in the future too. So we have a lot of Vs.....and we still have one A
- That A stands for analytics. Collecting more data that meet one or more of the Vs mentioned is still worthless if you don’t do anything with it. Analytics are required to generate the insights that enable improved decision-making. So, that’s Big Data. And I just want to stress at this point that although we do have many Vs that define BD, none of these Vs have a threshold nor is there a threshold to the number of Vs that needs to be fulfilled to call something BD.....
- Then.....what is Precision dairy farming? Well, we have heard a lot today about this topic, so this slide is just a brief summary of what PDF is.
- I have been working in this field of PDF the past years, and when I just started to hear from Big Data, and saw the enthusiasm that this word caused by many people, I couldn’t really understand the fuzz around it. I figured that my research within the field of Precision Dairy Farming was basically Big Data.
- I mean, when working with PDF technologies, you work with large amounts of data generated by sensors
- You are already working with data that are generated at high speed, true. For example, with automatic milking, electrical conductivity can be measured and recorded every second..... But....the need for realtime processing and descision making in PDF area so far has not been that much of an issue.... Based on sensor measurements in robotic milkings, the decision to automatically draft milk with blood is probably the only example where a decision has to be taken immediately (after milking). All other events monitored by technologies do not require instant actions.
- And with the PDF technologies you have to face the challenges with differences in veracity? Activity from one sensor technology is different from another technology and in you’re analysis, and in the interpretation of it, you have to know these differences and the consequences of it. And by using PDF technologies, we often have tools that monitor stuff, so yup, we can tick that box, or circle too, perhaps with a somewhat lessen extend.... All the other Vs that define Big Data, are not yet used by PDF, or to a very basic or small extend.
- So by thinking about this....and by looking at this picture I came to realize that PDF is not Big Data. It certainly has key elements from BD, and new analytical tools like machine learning or data mining are required to understand and visualize the data, but they are different And although I just mentioned that there is no threshold set at the number of Vs that need to be fulfilled before you can call something BD I wasn’t so much impressed by This picture myself.... So apparently, I have not been doing as much of Big Data than I thought before...
- Thinking about this a bit longer, I would like to highlight some more differences between BD and PDF. Now, why is this important to recognize? Because I belief that is you think you’re doing something that is actually not Big Data, you will stick with the same procedures and methodologies to collect preprocess and analyse data, you stick with the same approach to define research questions and by doing so you will not make the progress that Big Data is actually offering.
- But, as mentioned earlier....Big Data is buzzing around, and developments in sensor technologies, or precision dairy farming, are pushing these developments of Big data in the livestock sector too. I’d like to explain three projects we are currently working on at WUR that are related to dairy, and that all work in the Big Data field. For each of these projects, I briefly explain the goal, and I would like to demonstrate why these projects are more than just PDF
- Plaatje onderzoeksvoorstel
- 1) multidisciplinary scientific expertise in genomics, environmental assessment, nutritional physiology, health management, precision livestock farming, mathematical modelling, and socio-economics; 2) partners and stakeholders representing breeding organisations, farm technology companies, farm and veterinary advisory services, and farm sectors (organic, grazing, etc.); and 3) a unique data basis including >1 million genotypes