Sensors to monitor vineyard Tendencies

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1 Christophe GUIZARD IRSTEA Sensors to monitor vineyard Tendencies UMR ITAP Montpellier InnoVine Final symposium Toulouse, of November 2016

2 InnoVine Final symposium Toulouse, of November 2016 PRESENTATION PLAN What is the Need? Emerging technologies Examples Conclusion

3 What is the Need? (Not exhaustive) InnoVine Final symposium Toulouse, of November 2016

4 Farm Ecosystem Farm size World market Human resources An ecosystem very complex Lot of actors Lot of rules Lot of exchanges New business New demands InnoVine Final symposium Toulouse, of November 2016

5 InnoVine Final symposium Toulouse, of November 2016 What We Need New tools? YES, but do do what? Simplify the farmer life Meet regulatory and business requirements How to do? Use new technologies to have an objective information for better decision Technologies must be Easy to use, to install, to maintain Efficient Cost effective With a fast Return Of Investment

InnoVine Final symposium Toulouse, 16-17 of

6 InnoVine Final symposium Toulouse, of November 2016 Future Look like? Statistiques

7 Emerging technologies (Not exhaustive list) InnoVine Final symposium Toulouse, of November 2016

InnoVine Final symposium Toulouse, 16-17 of November 2016 Emerging trends Emerging trends on sensors 1. Optical sensors, lasers.

8 InnoVine Final symposium Toulouse, of November 2016 Emerging trends Emerging trends on sensors 1. Optical sensors, lasers... Contactless, non-contaminating, Real time Can «reproduce» human feeling Able to "see" the invisible (IR, NIR, UV...) 2. New information vectors Smartphones UAV, satellites IOT(sensors networks) New services Ressources management Cartography (biomass, soil, nitrogen...) Traceability Etc. System Heart

9 InnoVine Final symposium Toulouse, of November 2016 Emerging trends Farm Lab Observation SENSOR Plant vigor Pedestrian, Satellite tools Observation Soil Physicochemical components Plant stress Etc. Robots UAV Observation Action Weeding Spraying Tillage

10 Examples (Not exhaustive list) InnoVine Final symposium Toulouse, of November 2016

11 InnoVine Final symposium Toulouse, of November 2016 Place of sensors Knowledge and Understanding Diseases Maturity Load Diseases, Water Stress Traceability, Risk management, Resource management, Production management Berry Cluster Vine Vine plot Farm Territory Acting & Controling Pruning, Leafing, Treatments... Precision spraying, Maintenance inter row Harvesting, Logistics...

InnoVine Final symposium Toulouse, 16-17

(http://spensatech.com) Src: http://www.

12 InnoVine Final symposium Toulouse, of November 2016 Pests control BEECAM Pheromone dispenser for IPM (Canada) ZTRAP : Insect trap Orchards ( Src: SRC : SRC :

13 Pedestrian optical sensors GREENSEEKER Trimble QUALIRIS GRAPPE Sodimel (Prototype) SPECTRON Pellenc DUALEX Force-A SMARTGRAPPE (prototype) MULTIPLEX Force-A vitisflower InnoVine Final symposium Toulouse, of November 2016

14 Example Lab on the chip Allergy Test with a smartphone Test counterfeit (ie pills) Cholesterol test Colorimetric test Source :UCLA Henry Samueli School of Engineering and Applied Science MIT Techology Review (Photo courtesy of Stratio) Cornell University InnoVine Final symposium Toulouse, of November 2016

15 Embedded solutions InnoVine Final symposium Toulouse, of November 2016

16 Embedded solutions InnoVine Final symposium Toulouse, of November 2016

17 Vegetative Canopy measurement InnoVine Final symposium Toulouse, of November 2016

18 InnoVine Final symposium Toulouse, of November 2016 Embedded solutions Solution for traceability for spraying system Src : IRSTEA

19 InnoVine Final symposium Toulouse, of November 2016 Wireless network sensors Lot of solutions on the market System TracoVino Humidity Soil Humidity Temperature Solar Radiation Can be extended : Additional sensors for Leaf Wetness, Soil PH Values and Nutrient Levels

imagery for vineyard management Oenoview

«TerraDrone» For a centimetric precision

"free" images with coverage frequency of 5

20 InnoVine Final symposium Toulouse, of November 2016 UAV / Satellites «Aerial» imagery for vineyard management Oenoview (ICV) resolution <= 2,5 m UAV example «TerraDrone» For a centimetric precision New European satellites "Sentinels provide "free" images with coverage frequency of 5 days (forecast), and metric resolution SEQUIOA sensor Parrot

21 New sensors for new services Conclusion Changing practices, methods, and often the organization Next revolution? New IOT are everywhere «from the vineyard to the bottle» A gateway to robotics in the vineyard Towards true collaboration Human & Machine (Smart Machines) New challenges for Research Improve sensors performance Build new agricultural knowledge with new data from new technologies Ensure safety of users data InnoVine Final symposium Toulouse, of November 2016

22 Mobility changes everything in business, but it doesn t replace everything. Thanks for your attention Contact Christophe.guizard@irstea.fr La France Agricole InnoVine Final symposium Toulouse, of November 2016

24 InnoVine WP5 Toulouse, 16 & 17 of November 2016 Christophe GUIZARD IRSTEA Smartgrappe A smartphone in a vineyard

to the scene Reference targets inside Work on any smartphone (IOS, Android, Win10 ) S

25 Smartgrappe concept Support + Mobile + software = SMARTGRAPPE P a t e n t e d a p p a r a t u s L o w c o s t s u p p o r t Controlled lighting conditions Constant distance to the scene Reference targets inside Work on any smartphone (IOS, Android, Win10 ) S o f t w a r e Berries size, color and surface defect Number of berries / surface unit 2

26 Cloud solution Statistiques Applications 3

27 Robust detection Blooming Grappe variety Berries Size 4

28 Results Images Samples 5

29 Conclusion Smartgrappe A simple and low cost solution to help you to manage your vineyard with your smartphone 6

30 THANKS! See you later 7

31 GRAPEVINE OPTICAL SENSORS AND DECISION SUPPORT TOOLS FOR PRECISION VITICULTURE NAÏMA BEN GHOZLEN JEAN-LUC AYRAL INNOVINE International Symposium 17 November

32 ACTIVITIES Expertise in optical technologies: fluorescence and plant-light interactions Manufacturing of proximal plant sensors DUALEX MULTIPLEX Sales of plant sensors to Research Institutes Sales of diagnostic services to wine growers

33 A FULL RANGE OF VINEYARD DIAGNOSIS Vineyard Management Actions Spring FA-vigor Leaves Vigor - Physiological balance In-season Nitrogen fertilization Summer Autumn FA-vendange Grapes Maturity monitoring Harvest Quality analysis Zones optimization Selective harvest Homogeneous batches Winter FA-wood Pruning woods Vigor Pruning, fertilization, grass cover

34 DATA PROCESSING AND DISPLAY Data display on 2 complementary interfaces SENSOR Capteur : Real-time Indices en vine temps indices réel FA-SERVER Mapping and dynamic zoning 4

35 GRAPEVINE VIGOR MAPPING FA-vigor Foliar density map Diagnostic map Vigor map (NBI index) Cru classé 1855, appellation Margaux Display the balanced zones and the origins of the unbalances (N carency, )

classification Intermediate High potential Low

36 MASTERING GRAPE MATURITY FA-vendange Anthocyanin evolution Measurement of anthocyanin content Plots classification Intermediate High potential Low potential Optimize harvest logistics and understand wine phenolic potential

37 MASTERING GRAPE MATURITY FA-vendange Zone Anthocyanin mapping mg/l Anthocyanin zoning along the rows Cru classé 1855, appellation Margaux Anthocyanin level % area Average value High mg/l Low mg/l

vigor and grapes quality maps to identify zones for

38 Anthocyanins MASTERING GRAPE MATURITY FA-vendange Zone «Premium» Vigor Lower Optimum Higher Combine the leaf vigor and grapes quality maps to identify zones for premium wines Higher Lower Cru classé 1855, appellation Margaux

39 THANK YOU FOR YOUR ATTENTION INNOVINE International Symposium

40 InnoVine Final symposium Toulouse, of November 2016 New tools and applications for phenotyping and vineyard monitoring Javier Tardaguila and Maria P. Diago

41 New grapegrowers need more data

42 From visual assessment to data-driven viticulture.

43 Vineyard monitoring and plant phenotyping

44 New technologies for phenotyping Hyperspectral imaging Spectroscopy Computer vision

45 Hyperspectral imaging, a powerful tool for phenotyping VIS-NIR Hyperspectral camera

Classification of cultivars and clones using hyperspectral imaging Diago, M.P., Fernandes, A.M., Millan, B., Tardaguila, J., Melo-Pinto, P 2013.

46 Classification of cultivars and clones using hyperspectral imaging Diago, M.P., Fernandes, A.M., Millan, B., Tardaguila, J., Melo-Pinto, P Computers and Electronics in Agriculture 2013 Fernandes, A, Melo-Pinto Millan, B., Tardaguila, J. Diago, M.P., Journal of Agricultural Science 2015

squares (MPLS) were built for 14 individual anthocyanins with

47 Using hyperspectral imaging to fingerprint individual anthocyanins Predictive models based on modified partial least squares (MPLS) were built for 14 individual anthocyanins with coefficients of determination of cross validation (R 2 cv) ranging from 0.70 to 0.93

48 New tools to non-destructively estimate the number of flowers and berries using computer vision

49 Computer vision in viticulture

51 Estimation of berry number per cluster by computer visión in the field Original image Extraction of berry candidates Final result after false positive filtering

Actual number of berries per cluster Estimation of berry number per cluster by computer vision 300 250 y = 1.

52 Actual number of berries per cluster Estimation of berry number per cluster by computer vision y = x R² = 0.81** Number of berries visible in the image

53 Fruit-set assessment by computer vision Goal: To investigate differences on fruit-sett among cultivars Non-invasive, inexpensive, image analysis as powerful tool to estimate fruit set

54 On-the-go assessment of canopy porosity and exposed clusters in precision viticulture

55 Multi-sensor mobile platform was developed Televitis mobile lab Speed: 5 km/h Illumination for night time image acquisition GPS for data georeferencing Inductive sensor and controller for autonomous camera triggering

56 Image aquisition and processing 1.Image obtained on-the-go using a quad 2.Image segmentation Porosity (White) Clusters Leaves

% porosity from image analysis on-the-go On-the-go assessment of canopy porosity 60 50 y = 0.

57 % porosity from image analysis on-the-go On-the-go assessment of canopy porosity y = x R² = % porosity with Point Quadrat (reference value)

58 On-the-go assessment of wood pruning using machine vision

59 On-the-go imagen adquisition and processing Original image Segmented image

60 Pixel number of wood On-the-go assessment of pruning weight y = x R² = 0.92** ,0 0,2 0,4 0,6 0,8 1,0 1,2 Pruning weight (Kg/vine)

61 Mapping Embedded sensors with GPS for data georeferencing Pruning weight (kg/vine)

62 Javier Tardaguila María Paz Diago Juan Fernández Borja Millán Salvador Gutiérrez televitis.unirioja.es

63 Thank you for your attention

high resolution remotely sensed images for precision

64 L innovation au service de la Protection du Vivant Spatial Presentation of WINEO s products - Using Very high resolution remotely sensed images for precision viticulture Environnement Développement durable Innovation technologique

65 WINEO products NOVELTIS proposes the acquisition of suitable remotly sensed images over your vineyard; NOVELTIS proposes to digitize vineyard boundaries. High resolution images Mosaic over the vineyard and digital boundaries of the parcels NOV-9DAC-FRA-SL-1014 NOVELTIS 2015 Ce document est la propriété de NOVELTIS et ne peut être reproduit ou communiqué sans son autorisation 2

Several innovative products have been developed and validated by NOVELTIS for precision viticulture: Map and characterization of vineyard rows ; WINEO products Map of missing

66 Several innovative products have been developed and validated by NOVELTIS for precision viticulture: Map and characterization of vineyard rows ; WINEO products Map of missing crops. Percentage of missing crops within 5% NOV-9DAC-FRA-SL-1014 NOVELTIS 2015 Ce document est la propriété de NOVELTIS et ne peut être reproduit ou communiqué sans son autorisation 3

on detected vines; WINEO products High Medium Low

High vigour Low vigour NOV-9DAC-FRA-SL-1014 NOVELTIS 2015 Ce

67 Several innovative products have been developed and validated by NOVELTIS for precision viticulture: Map of vine vigour only on detected vines; WINEO products High Medium Low Classification along homogeneous areas. High vigour Low vigour NOV-9DAC-FRA-SL-1014 NOVELTIS 2015 Ce document est la propriété de NOVELTIS et ne peut être reproduit ou communiqué sans son autorisation 4

Password Your layers http://innovine.noveltis.

68 WINEO products Several available output formats. KMZ/KML files (can be visualized on Google Earth) Webmapping interface Login / Password Your layers NOV-9DAC-FRA-SL-1014 NOVELTIS 2015 Ce document est la propriété de NOVELTIS et ne peut être reproduit ou communiqué sans son autorisation 5

69 Contact NOVELTIS creates added value for its customers by supplying innovative, tailored solutions. Florian POUSTOMIS Scientific engineer Tél. : +33 (0) NOV-9DAC-FRA-SL-1014 NOVELTIS 2015 Ce document est la propriété de NOVELTIS et ne peut être reproduit ou communiqué sans son autorisation 6

71 vite.net a DSS for sustainable vineyard management Powdered by

Core activity Development of Decision Support Systems (DSSs) for

72 Mission Increase the value of research by transferring the technological innovation to practical agriculture. Core activity Development of Decision Support Systems (DSSs) for sustainable crop production based on new Information and Communication Technologies (ICTs).

73 Structure Information on crop & environment feeds the DSS in real time Web applicaiton Holistic approach Usefulness Strengths

74 How it works

75 Structure Web applicaiton No software installation, constant updating of the functionalities Holistic approach Usefulness Strengths

76 Web-application 7/7 24h

77 Structure Web applicaiton Holistic approach Considers all the key cultivation problems Usefulness Strengths

u Vine growth & development u Ripening & yield Plant u Berry mouth u Leafhopper u Mealybugs Protection u Downy mildew u Powdery mildew u Grey mold u Black-rot u OTA u PPPs u Optimal dose u

78 u Vine growth & development u Ripening & yield Plant u Berry mouth u Leafhopper u Mealybugs Protection u Downy mildew u Powdery mildew u Grey mold u Black-rot u OTA u PPPs u Optimal dose u Protection dynamic u Resistance risk Canopy management Stress u Frost u Drought u Topping u Leaf removal u Cluster thinning Cropping u Fertilization u Weed control u Irrigation Functionalities

79 Structure Web applicaiton Holistic view Usefulness Complex and multi-criteria decisional processes are turned into easy, clear & reliable decision supports Strengths

80 Frequency (%) Less time spent Easier decision-making Better awareness Better knowledge Better decisions Score Friendliness Speed Clarity Confidence Usefulness Usefulness: user s feedback

of protection: -195 /ha IPM TFI / season: - 30-40% Cost

81 Same protection than the grower s schedule with important savings: Organic viticulture Kg copper/season: - 37% Cost of protection: -195 /ha IPM TFI / season: % Cost of protection: /ha Usefulness: economic benefits

82 Conventional vite.net IPM vite.net organic Usefulness: environmental benefits

83 Spain Portugal Greece Israel : 66 users 2015: 180 users 2016: 300 users

84 Thank you for your attention

1. Continuing the development and validation of mobile sensors. 3. Identifying and establishing variable rate management field trials

1. Continuing the development and validation of mobile sensors. 3. Identifying and establishing variable rate management field trials Project Overview The overall goal of this project is to deliver the tools, techniques, and information for spatial data driven variable rate management in commercial vineyards. Identified 2016 Needs: 1.