Smarter Farming When & How? M… · anacht - Nov 2016 ID ? • [email protected] • Congo - Belgium -...

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Smarter Farming When & How?

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ID ?• [email protected] • Congo - Belgium - Spain -

France - USA • Economy, Philosophy,

Law, MBA • International career • Strategic consultant • Change agent • 5 languages

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The world is Changing Rapidly

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Top 20 airports 2000

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Top 20 airports 2014

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Local Tastes - Global Brands

2012 off farm sales of Milk: $35 billion6

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Where food comes from…

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Copyright M

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Laws of TechnologyGordon Moore

Roland Haitz

Martin Cooper

~1985 ~1995 ~2005 ~2015 ~2025Analog GSM 3G

EDGE4G-LTE 5G8

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In 2007… a new world> 1 billion transistors microchip The iPhone Tablet operating system enabling “big data” for open-source software separate platform Facebook open to anyone Airbnb service

Source: Thomas L. Friedman MAY 3, 2016

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http://www.nytimes.com/column/thomas-l-friedman

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From Precision Agriculture

to Smarter Farming

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U.S. Patent Oct. 15, 1996 Sheet 1 of 47 5,566,069

FIG]

u|||| ||||||||||| um IIIIIIIJIIQIO 11151111) 1191 lllll ||||| ||||||||| |||n |||| I

UIllted States Patent [19] [11] Patent Number: 5,566,069 Clark, Jr. et al. [45] Date of Patent: Oct. 15, 1996

[54] COMPUTER NETWORK FOR COLLECTING 5,063,505 11/1991 Pate et a1. . AND ANALYZING AGRONOMIC DATA 5,173,079 12/1992 Gerrish ...................................... .. 460/7

5,173,855 12/1992 Nielsen et a1. .

[75] Inventors; Louis G_ Clark, Jr” St Charles; McCann et a1. ........................ .. 239/11 Michael E. Corum, St. Louis; Donald ’ ’ R. Gummow’ Jr” oTanon; Jeffrey L. 5,379,057 1/1995 Clough et a1. ........................ .. 345/173

McK‘me’ ROM; Ma" Vanacht, 59 Primary Examiner~Donald E. McElheny, Jr. Lows’ all of M0‘ Attorney, Agent, or Fimz—Gordon F. Sieckmann

[73] Assignee: Monsanto Company, St. Louis, Mo. [57] ABSTRACT

[21] App1_ NOJ 207,539 A client-cliept-sgrver comtpgterbnetwpirli incltlidtes a piortlaple computer 0 ten connec a e y a a a 1n 0 a es op [22] Filed? Mar- 7, 1994 computer (client) which transmits data through an offline

[51] Int. c1.6 .................................................... .. G06F 19/00 a sgav?erzgier‘gfaf‘gctgf [52] Ufs' Cl’ 364/420 tural data generated Ehrough tghe operatign 0% a fagrm. A

Fleld of Search ................................... .. of clienpclient Computer sub_natworks, one for 364/403 each of a plurality of farms, is anticipated for connection

, through an offline, batching data link to a single system [56] References cued server such that a master data base may be assembled of data

Us‘ PATENT DOCUMENTS for many farms. Real time, same growing season, analyses and re orts ma be enerated for use b an in ividual

R59 gall, III . farmerpfrom thig, mastger data base. The pgrtable computer , , ayner . - . . .

4,015,366 4/1977 Hall, HI _ Eplgdes a nestectl displaly dI‘IVC; softwlarlglfoia'it iraphtic ttlsfr 4,176,395 11/1979 Evelyn-Veere et al. . _ e "flee comp“ er’ S“ as a mom? 1g‘ _ SS S Fm’ 0 4,209,131 6,1980 Barash et al _ simplify the data entry process. Various detallsof 1mple— 4,244,022 H1931 Kendall _ mentauon mclude scrollable lists WhlCh have entrles that are 4,430,328 211984 Oglcvce er a1_ _ context sensitive to ehrmnate improbable or redundant or 4,463,706 8/1984 Meister et a1. ..................... .. 119/5102 incorrect selections. The desktop computer includes a pro 4,493,290 1/1985 Gibbard ----- -- - - gram for use on a Windows platform which closely emulates

Hayes .................................... .. the graphic user inter_face provided the portable Com 4’626’984 12/1986 Unmh 6‘ a1‘ ' puter such that a farmer need only become familiar with one 4’722’054 H1988 YOIOZ“ et a1‘ ' computer program to fully operate the system The o?line 4,745,550 5/1988 Witkin et al.. b t hi d t Yuk h. h b m . 4,755,942 7/1988 Gamma et a1__ ac‘ ng aa_1 ,w 10 may e acommerc1 _ essagmg 4,760,547 7/1988 Duxbury _ service, permits a smgle system server to eiliciently com 4351’999 7/1939 Mon-yama _ mumcate with many farmers w1thout sacn?cmg s1gn1?cant 4,931,932 6/1990 Dalnekoft et al. . amounts of system server processing time to input or output 4,949,248 8/1990 Caro. data. 4,992,940 2/1991 Dworkin. 4,992,942 2/1991 Bauerle et al. . 5,023,787 6/1991 Evelyn-Veere . 55 Claims, 47 Drawing Sheets

mean Sad: tour

Arusum:

‘See! ‘Tinted: v5

Depth:

SeelLlld:

Aau

Inchn

~20 years ago

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Dream in1995

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Reality in 2015

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Ag-Food Supply chain

Transformation, Marketing, Sales, Distribution

Farm Operations:Equipment, Contracting, Transport ...

Variable Inputs:Feed & Pharmacy, Seeds & Biotech, Chemicals, Fertilizer ...

Real Capital Goods:Land, Plantations, Animal Confinement, Rail lines, Elevators, Terminals, Depots ...

Financial Services: Risk Management, Seasonal credit, Investment capital ...

Information Services: Weather, Environmental Data, Royalties, Traceability, Certifications ...

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Precision Agriculture 1995-2015







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Smart Agriculture 2015-2035







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Future AgricultureIndustry Current Names Trend New entrants?

Chemicals & Seed Big 6: BAY, MON, SYN DUP, DOW, BASF

Big 4: Baymon, DuDow, SynChina, BASF CRISPR players?

Commodity ChemicalsMany (Belchim)

Chinese and Indian Further consolidation

Fertilizers Agrium, Potash, Mosaic, Yara, CF, Vale …

Agrium+Potash and Further consolidation

Green Ammonia (Siemens?)

Distribution & Retail Local, National & Continental players

National, Continental, World & online players

Consumer & Online retailers (Alibaba,

FBN…)

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Fine Chemicals Formulations, Adjuvants & surfactants

Evonik, Solvay…Aroma & fragrance industry

additive manufacturing?

White Biotech Novozymes, Chr. Hansen, DSM, Lesaffre Alliances Japanese, Korean,

Chinese

Red Biotech Example: Vestaron Many ?

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Agric. Equipment Deere, CNH, AGCO, Claas, Kubota …

Further consolidation, including 1-2 Chinese

Specialized hardware Many discrete players IMEC, Netafim Growing interest Panasonic, UTC, ABB,

NXP, U-blox,…

Optical & sensing equipment Zeiss, Leica, VITO Growing interest

Canon, Nikon, Minolta, Hamamatsu, miniaturized

hyperspectral imaging

Phenotyping Lemnatech (lab. level) Field level phenotyping Bosch, Nercita ?

Automation & Robotics Lely, Naio Growing interestBosch, Honeywell, GE (Predix), Ublox, Samsung-Harman

System integrators Deere, Trimble, Topcon, Nercita Growing interest Hitachi, Intel, ARM

Data Integrators Monsanto, SST, Agrian, Hitachi, Nercita

+ Mitsui, GlobalGAP, SGS, FBN, Microsoft…? Panasonic?

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Five topicsBig Data & IoT

Automation & AgRobots The Sun House

Food waste Agricultural Innovation Clusters

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Funny !

Students in Shenzhen use 3D printers to create unique / customizable food. (Shenzhen Makers week, Oct 13, 2016)

Useless ?

Think… Barilla, Delacre, Lotus, Guylian, Neuhaus …

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Big Data & IoT

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Big Data & IoT

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IoT Implementations• Preventive maintenance

• Rolls Royce engines • Fleet Management

• UPS • Asset surveillance

• “the Internet of Cows” • Customer service

• Mercedes Benz M-Brace

• Customer segmentation • Johnnie Walker Blue

Label usage tracking • Walt Disney World

priority customers • Fitness First personal

training

Computerworld Uk, April 25, 2016

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Ag. IoT Today• Integrated hi-tech greenhouses / vertical farming

• Integrated wheat harvesting in Hokkaido (Hitachi)

• Sensor networks for quality driven wine production

• Eggs & Broiler production house management systems (Porphyrio)

• Use of vehicle telemetry to improve logistics and efficiency of field operations

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Ag IoT Next ?• Next generation of Controlled Environment

Agriculture (green houses and ‘vertical farms’)

• Integrated supply chains for … meat, fish, fresh fruit & vegetables, hops & beer,

• SPC (Statistical Process Control) to monitor growth of crops & animals, production of fermented products and food processing…

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Requirements for IoT• Wireless networks…

• 99.99 % reliable ( < 10 sec down / 24 hours)

• broad band ( > 100 Mbps, < 10 millisec latency)

• Data standards / system integration

• An ‘operating system’ (GE Predix ?) or ‘open source’

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Building blocks?

! Cart" Sign In or Register # English $ALL $ %

NXP Automotive Products &

Automotive Products Design Resources

Automotive Products

'

Microcontrollers and Processors

MCUs and MPUs for ADAS, Safety,Security, Powertrain and Body

Electronics

Secure Car Access

Security transponders andencryption for secure car access

Interface andConnectivity

CAN, LIN, Flexray,SBCs, Ethernet, USB

Media and AudioProcessing

Multimedia solutions for in-car entertainment

Discrete and Logic

Dedicated automotive solutionsPower Management

PMICs and LED lighting controls

RF

Advanced performanceand simplified design

Sensors

Angle, rotational speedand temperature sensors

You’ll go a long way to findan automotive portfolio withthe breadth, depth andquality of NXPSemiconductors’. It coversdiverse areas, from in-vehicle networking toautomotive lighting, carradio and audio toautomotive power, andtelematics OBU to accessand immobilization

®

Discrete and Logic forAutomotive

NXP is the global leader inautomotive MOSFETs and aglobal supplier of small-signal discretes. Our AECQ100 qualified logicproducts meet thedemanding requirements ofautomotive applications

Identification and Security()

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Documentation((

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What's NewNew Automotive S32R RadarMCUOffering higher accuracy andsafety allows the vehicle to detectand classify objects, enabling anew range of semi-autonomouscapabilities

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NXP Sensors &

Sensors Design Resources

Documentation

Software and Development Tools

Sensors

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MMA955xL, MMA9553L, MMA9555L,FXLC95000CL

Gyroscopes

FXAS21002C

6-Axis Sensors

FXOS8700CQ 3DAccelerometer

Touch Sensors

CRTOUCH MCF5227x,i.MX233, i.MX255, i.MX257,i.MX258

Magnetometers

MAG3110 family

Accelerometers

MMAxxxx family

Pressure Sensors

FXTH87, MPXH6101Axx,MPXH6400Axx, MPL3115A2, MPXx5004

Angular Sensors

KMA2xx/KMZxx families

Temperature Sensors

KTY8x/PCT2xxx families

I²C Temperature /Voltage Monitors

I²C Fast-mode Plus

Rotational Sensors

KMI16 and KMI17

Capacitive Sensors

PCA8885TS, PCA8886TS,PCF8883, PCF8885

Expanding on its more than30 year sensing history,NXP has unveiled a newera of sensing with theSensing Solutions portfolio.This new brand of sensorsis designed with the rightcombination of intelligentintegration, logic andcustomizable software onthe platform to deliversmarter, more differentiatedapplications.

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By 2020, IoT will be a normal part of automated systems

& supply chains.

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Automation & AgRobots

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Copyright M

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AgRobots: Good progress• Automated navigation and ‘self-driving’ field

vehicles

• Accuracy of applications of chemicals (individual nozzle control)

• UAV’s for data gathering and for applications

• Milking robots in the stable, and in the pasture

• Lab based phenotyping

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AgRobots: Slow progress• Coordinated fleets of vehicles

• Fruit & Vegetable Harvest: humans most often outperform humanoids in unstructured settings

• Weeding robots

• Growth tracking (field level phenotyping)

• Many concepts & prototypes

• How much longer? 5 -10 - 15 years depending of application

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Cars & AgricultureP.A. Now Cars Now Self driving

CarsWhen ? 2000 2005-10 2020-25

Accuracy 2-5 cm 1-2 m 2-5 cm

Operating speed 15 km / h 150 km / h 150 km / h

Enabler RTK Integration High level integration

Data transmission Little & slow Real time G3-G4-LTE Real time G5

Data load 10-100 MB 4 GB 10-20 GB

Initial cost $70,000 $3-5000 $5-10,000Current cost $20,000 $2-3000 $3-5000

Adoption Broad in US High end Luxury & new brands

The car industry will offer better specs at a fraction of the cost! This WILL have implications for agriculture.

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Recent mergers: Samsung + Harman - Qualcomm + NXP - SoftBank + ARM - Avago + Broadcom

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“Thinking” RobotsCompany Product Activity Business outcome

Narrative Science Quill a.i. Analyze raw data Human like natural

language reports

Amazon Kiva Robots

Plan, navigate & coordinate

Fulfill warehouse orders

IBM WatsonAnalyze symptoms,

diagnoses and available science

Suggest treatments for specific ailments

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Engineering & Biology• The environment (soil, water, air) . . = biology • Seeds, crops & farm animals . . . . = biology • Pest & diseases . . . . . . . . . . . . . . = biology • Harvesting & processing food . . . = biology • Human & pet consumers . . . . . . . = biology

Move beyond ‘engineering certainties’ to enjoy the rich diversity of biological systems

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“Thinking” solutions in Ag.• Huge amount of data needed to capture

• the complexity of biological systems exposed to unpredictable weather & a variable environment

• The tenuous interaction between fertilizer input efficiency and water quality

• People: digital divide between

• innovators vs. slow adopters,

• continuously re-trained vs. obsolete users

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A new, centuries old concept …

The Traditional Chinese Sun House

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The Sun House

• Long ‘wall’ on the north side as heat sink

• Angled black roof reduces the heat from the summer sun

• Curved metal structure supporting a plastic cover

• 80 - 100 m long,

• 8 - 10 m wide,

• 600-1000 m2

• 2.5-4 m high

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In it’s simplest form…

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To a large scale operation…

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Getting an early start …

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Floating hydroponics…

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Additional energy efficiency

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At current levels of technology, Sun Houses can compete in China. They need to be tested in the West

LIGHT:

• Sun Houses use sun light like open air farming

• LED’s can be used to lengthen daylight in deep Winter & gain 3 months productive time

• colored lights (red & blue) can be used as a complement, if they prove to be useful

WATER:

• existing systems of drip irrigation, hydroponics and aeroponics can easily be integrated in Sun Houses

• Further progress in science and engineering will be integrated

CYCLES:

• Sun houses in China are used for a wide variety of crops

• improved modern seed germ plasm are used in Sun Houses in China

• Further progress (biologicals, enzymes, hormones…) is likely, and will be integrated

ENERGY:

• Sun Houses use the north wall and angled black roof to buffer heat & cold and dampen temperature extremes

• simple Sun Houses stretch the growing season to ~ 8 months

• adding light & heat pumps in Winter, and heat pumps in Summer can push Sun Houses to 12 months of production

Well equipped Sun Houses in China cost $80-100 k / 600 m246

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Food waste…Jean C. Buzby and Hodan F. Wells

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throw out the extras. Figure 3.1 provides an example of broccoli loss at different stages.

ERS’ food loss estimates are only for the retail and consumer levels in the United States because of data limitations. Nationally-representative data on total food loss at the farm level and between the farm and retail levels are unavailable.

WHAT ARE THE CONSIDERATIONS AND INCENTIVES CONCERNING FOOD LOSS?

Some loss is inevitable because food is inherently perishable, and spoiled or deteriorated food must be discarded to ensure the safety and wholesomeness of the food supply. For example, restaurant leftovers not taken home by patrons are appropriately discarded out of health considerations. Also, some meat, poultry, and other foods are recalled when there are health or safety concerns.

Individual tastes and preferences also come into play for consumers. For example, some people may not like to eat the crusts on their sandwiches or don’t like or get around to using leftovers. Given the number of calories and overweight people in the United States, it would be detrimental for everyone to eat all the food that they are served or buy.

Economic factors may only provide limited incentives to reduce food loss, that is, some amount of loss may be economically justifiable. For example, it may not be worthwhile for a supermarket to pay for the labor and other costs to monitor and mark down foods as they approach the sell-by dates when considering the lower price they then might receive, and other factors.

Additionally, there are often tradeoffs between the advantages of using technologies that reduce loss and any disadvantages of using the technologies.

3.1 Broccoli loss in the food supply chain

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IOT & Robots can help !Jean C. Buzby and Hodan F. Wells

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throw out the extras. Figure 3.1 provides an example of broccoli loss at different stages.

ERS’ food loss estimates are only for the retail and consumer levels in the United States because of data limitations. Nationally-representative data on total food loss at the farm level and between the farm and retail levels are unavailable.

WHAT ARE THE CONSIDERATIONS AND INCENTIVES CONCERNING FOOD LOSS?

Some loss is inevitable because food is inherently perishable, and spoiled or deteriorated food must be discarded to ensure the safety and wholesomeness of the food supply. For example, restaurant leftovers not taken home by patrons are appropriately discarded out of health considerations. Also, some meat, poultry, and other foods are recalled when there are health or safety concerns.

Individual tastes and preferences also come into play for consumers. For example, some people may not like to eat the crusts on their sandwiches or don’t like or get around to using leftovers. Given the number of calories and overweight people in the United States, it would be detrimental for everyone to eat all the food that they are served or buy.

Economic factors may only provide limited incentives to reduce food loss, that is, some amount of loss may be economically justifiable. For example, it may not be worthwhile for a supermarket to pay for the labor and other costs to monitor and mark down foods as they approach the sell-by dates when considering the lower price they then might receive, and other factors.

Additionally, there are often tradeoffs between the advantages of using technologies that reduce loss and any disadvantages of using the technologies.

3.1 Broccoli loss in the food supply chain

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Ag-Food Sector Companies

Innovation Communities Incubators

Companies & Institutions

Networking Assets

Investors & Capital Markets

Ag-Food Sector R & D & E Partners / Facilities

Research Capabilities

Agriculture & Food Innovation ClusterGeneric

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Innovation Communities

Incubators


Networking Assets


Ag-Food Sector R & D & E

Partners/Facilities

ResearchCapabilities

AB Inbev, Monsanto, KWS, Bunge, Nestle-Purina, Panera,

ABMauri ABF, Novus, Post Foods, Dupont Nutrition & Health, Danisco,

ICL, MFA

Enterprise, Boeing Defense, Express Scripts, NGA,

Emerson, Scott AFB, Wells Fargo Advisers, Edward Jones, TDA Scottrade

BRDG Park, Helix, Cortex, CIC, T-Rex

InfoAg Conference, Ag Innovation Showcase, Agri-

Business Club, MVF, VentureCafe, Nat. Corn Growers Assoc., Am.

Soybean Assoc., Academy of Science, ItenMonsanto Growth Ventures,

Cultivation Capital, MTC, Biogenerator, Lewis & Clark Ventures, Prolog Ventures, Arch Angels, Advantage

Capital, Yield Lab Accelerator, iSelect

Danforth Plant Science Center, MO Botanical Garden- Plants

Database, Fisher Delta Research Center, Columbia,

Washington University, St Louis University, University

of Missouri - St Louis,

St. Louis

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Agriculture & Food Innovation Cluster

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Innovation Communities

Incubators


Networking Assets


ResearchCapabilities

Ag-Food Sector R & D & E

Partners/FacilitiesAgriculture & Food Innovation Cluster

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Vlaanderen

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Er is toekomst Er is potentieel

Er is werk te doen

Heel erg fel bedankt !52

Smarter Farming When & How? M… · anacht - Nov 2016 ID ? • [email protected] • Congo - Belgium -...

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Transcript of Smarter Farming When & How? M… · anacht - Nov 2016 ID ? • [email protected] • Congo - Belgium -...