club of bologna · new technologies to improve pesticide application in vineyard. decision support...

NEW TECHNOLOGIES TO IMPROVE PESTICIDE APPLICATION IN VINEYARD.

DECISION SUPPORT SYSTEM, ULTRASONIC SENSORS AND LIDAR SENSOR

CLUB of BOLOGNAGiuseppe Pellizzi PhD Prize 2012

23rd Annual Meeting of the Club of BolognaNovember 9, 2012

Jordi Llorens CalverasEmail: [email protected]

SPACE FOR PHOTO

NEW TECHNOLOGIES TO IMPROVE PESTICIDE APPLICATION IN VINEYARD. DECISION SUPPORT SYSTEM, ULTRASONIC SENSORS AND LIDAR SENSOR

Introduction

Objectives

Experimental work

Conclusions FONT: USUC P0138 University Extension Service, 1919.

FONT: Balven

Introduction


Global vision about legislative aspects in water reference and plant

protection products

Directive of Sustainable Use of Pesticides 2009/128/EC

Water Framework Directive (WFD) 2000/60/EC

Authorization and commercialization of plant protection products 2009/1107/EC

Revision of Directive of Machinery for Pesticide Application 2009/127/EC

Tendency: increment attention in the phase of utilization of PPP

3


Community actuation for the sustainable use of pesticide

Directive 2009/128/ECof the European Parliament and of the

Council of 21 de Octubre de 2009

Regulatory European framework


RD que estableix el marc d’actuació per aconseguir un ús sostenible dels Plaguicides

RD per la inspecció d’equips d’aplicació de fitosanitaris en ús

Pla

d’Ac

ció

Nac

iona

l

Regulatory Spain frameworkN

atio

nalA

ctio

nPl

an


Crop height (LWA)

Crop width (TRV)

Density

Leaf area(LAI)

…

Crop characteristics: Key factor

Dose adjustment


Fruit crops Vineyard Soil CropsCitrus and

Olives

Àustria i Alemanya

kg/ha/m AC,màx. kg/ha

% màx. kg/hakg/ha/m AC,màx. kg/ha ---

Bèlgica kg o L /10.000 m2

LWA, màx. kg o L/ha --- kg/ha ---

França kg/ha kg/ha kg/ha ---

Països Baixos % màx, i L/ha --- % màx, i L/ha ---

Suïssa kg/10.000 m3 TRV % màx, i L/ha % màx, i L/ha ---

Noruega kg/100 m fila de vegetació --- --- ---

Grècia % màx, i L/ha % màx, i L/ha % màx, i L/ha % màx, i L/ha

Itàlia % mín. a màx. i L/ha % màx, i L/ha % màx, i L/ha % màx, i L/ha

Portugal % màx, i L/ha % màx, i L/ha % màx, i L/ha % màx, i L/ha

Espanya % màx, i L/ha % màx, i L/ha % màx, i L/ha % màx, i L/ha

Dose adjustment


Fruit crops Vineyard Soil CropsCitrus and

Olives

Austriai Alemanya

kg/ha/m AC,màx. kg/ha

% màx. kg/hakg/ha/m AC,màx. kg/ha ---

Bèlgica kg o L /10.000 m2

LWA, màx. kg o L/ha --- kg/ha ---

França kg/ha kg/ha kg/ha ---

Països Baixos % màx, i L/ha --- % màx, i L/ha ---

Suïssa kg/10.000 m3 TRV % màx, i L/ha % màx, i L/ha ---

Noruega kg/100 m fila de vegetació --- --- ---

Grècia % màx, i L/ha % màx, i L/ha % màx, i L/ha % màx, i L/ha

Itàlia % mín. a màx. i L/ha % màx, i L/ha % màx, i L/ha % màx, i L/ha

Portugal % màx, i L/ha % màx, i L/ha % màx, i L/ha % màx, i L/ha

Espanya % màx, i L/ha % màx, i L/ha % màx, i L/ha % màx, i L/ha

Dose adjustment


Adaptació de Wohlhauser, (2009)

Concentration%

Dosekg/ha

Dose by Height of Crop

kg/ha/m

Dose by Leaf Wall Area (LWA)(kg by 10000m2)

Application Volume(L/ha)

Height of Crop (m)

Distance between rows

(m)

Application Volume (L/ha)

Width of crop (m)

Dose by Volume of Crop (TRV)

kg/m3

Distance between rows

(m)Tree Fruit Dose Adjustment Discussion Group Meeting. Wageningen, The Netherlands. (2009)

Dose adjustment


Dose Decision Support System

SensorsConventional application

Adapted application


Introduction

Objectives

Experimental work


Objectives

FONT: Balven


Objectives

General objective

Improve applications of plant protection products invineyard by using these new technologies:

• Software of decision support system (DOSAVIÑA TEST).• Sensors for crop characterization (SENSORS COMPARISON AND

LIDAR MAP).• System for automatic volume adjustment application

(ADJUSTED APPLICATION).


Objectives

Specific objectives (DOSAVIÑA test)

• To present one methodology for validation Dosaviñasoftware.

• To validate the results of 4 years following therecommendations of Dosaviña in field conditions and indifferent wine regions.

• To determine in field conditions aspects such as quality ofdeposition, savings and biological efficiency.


Objectives

Specific objectives (Sensors comparison)

14

• To analyze the ability of LIDAR sensor and ultrasonicsensors to determine structural parameters of vineyard.

• To evaluate the applicability of ultrasonic sensors andLIDAR to map the structure of the vegetation in differentvarieties and stages.

• To compare measurements between manualmeasurements, ultrasonic sensors and LIDAR sensor.


Objectives

Specific objectives (LiDAR map)

• Configuration of a LiDAR and GPS system for canopy scanning.

• Development of a proper methodology to georeference thefrequency of LiDAR returns and its relation with canopycharacteristics.

• Validation of the methodology in different vine plantationsand situations.


Objectives

Specific objectives (Adjusted application)

• To investigate the amount of savings through theimplementation a system based on instantaneousmeasurement of vine volume.

• To evaluate the efficiency of this application compared to aconventional application based on land surface.


Introduction

Objectives

Experimental works


Experimental works

FONT: Balven


Dose Decision support system


Adjusted application

DOSA

VIÑ

Ate

stAd

just

ed

appl

icat

ion

LIDA

R m

ap

Sens

ors

com

paris

on


Experimental work(Dosaviña test)

Field validation of DOSAVIÑA, a decision

support system to determine the optimal volume rate for

pesticide application in vineyards

Publication data: Volume 35, Issue 1, June 2011, Pages 33-46

DOI: 10.1016/j.eja.2011.03.005Impact factor 2010(JCR): 2.455Category: AGRONOMYQuartile: Q1

Journal: European Journal of Agronomy


Field test in USA and Spain (2006-2009)

Estat de New York (USA)

Catalunya (ESPANYA)


DOSAVIÑA base of calculation

Objective: Leaf surface

Objective: impacts/cm2

K102

VMDπ34DLAI2V 7

3

iT ⋅⋅

⋅⋅⋅⋅⋅= −

EVV T

R =

EficiencySprayer

EficiencyWheather conditions

Data BaseVarietiesStagesPlaces

Pest/Disease PPP

Gil, (2003)Gil and Escolà (2007) Microsoft

Excel®


0 40 80m

L

Wc

Wd

Wb

Sample point of deposition

Sample point of efficacy


0

100

200

300

400

500

600

700

800

900

1000

App

licac

atio

n vo

lum

e (l·h

a-1) Conventional Dosaviña

Volu

me

appl

icat

ion

rate

(L/h

a)

Variety and crop stage

Savings in volum application.

40%





DOSA

VIÑ

Ate

stAd

just

ed

appl

icat

ion

LIDA

R m

ap

Sens

ors

com

paris

on


Ultrasonic and LIDAR Sensors for Electronic

Canopy Characterization in Vineyards: Advances to

Improve Pesticide Application Methods

Publication data: Sensors 2011, 11(2), 2177-2194

DOI: 10.3390/s110202177Impact factor 2010(JCR): 1.774Category: INSTRUMENTS & INSTRUMENTATIONQuartile: Q1

Journal: Sensors

Experimental work(Sensors Comparison)


AssayYears:

2008, 2009 i 2010

Vine varieties: Var. Cabernet SauvignonVar. TempranilloVar. Merlot

Crop stages (BBCH): 65, 75 i 85

Areas: Penedès i la Noguera

(Catalunya)


Ethernet (RJ-45)

Anal

ogic

Sig

nal

(0-1

0 Vd

c)

Pow

er S

uppl

y(2

4 Vd

c)

+ -

Pow

er S

uppl

y(2

4 Vd

c)

+ -

Seri

al P

ort (

RS-2

32)

LIDAR SICK LMS 200

ULTRASONICSENSOR

Siemens BERO COMPACT II 3RG6115-3GF00

COMPACT FIELDPOINT

National Instruments cFP-2120

PC CONTROLADVANTECH

ARK 3384

Data files:LIDAR DATA.TXTUS DATA.TXT

Analysis using:MATLAB® 7.0.1Microsoft ® Excel 2007

Software:LABVIEW® 8.5


LIDARSCAN® v.1

System configuration


Manual UltrasonicSensors LIDAR Sensor

MEA

SURE

MEN

TSCA

LCU

LATI

ON

S

Height Width LAI Width at different height

Width Height

TRV LWA Volume at different heights

Section surface

% ofgaps

Section surface

Volume Section surface

% ofgaps

Number of impacts

Data base for all comparison


Manual Ultrasonic Sensors LIDAR Sensor CHM CWM LAI CVM CWU CVU ZU CHL CWL CVL ZL IL

Man

ual

CHM 1 CWM 0.02 1 LAI 0.412 0.08 1 CVM 0.66 0.30 0.23 1

U.S

enso

r

CWU 0.55 0.04 0.42 0.09 1 CVU 0.55 0.04 0.511 0.09 1 1 ZU 0.66 0.00 0.441 0.13 0.77 0.77 1

LID

AR

CHL 0.23 0.37 0.29 0.641,3 0.44 0.37 0.33 1 CWL 0.09 0.00 0.00 0.00 0.05 0.05 0.00 0.06 1 CVL 0.21 0.01 0.221 0.01 0.57 0.572 0.38 0.37 0.16 1 ZL 0.26 0.00 0.361 0.07 0.41 0.41 0.542 0.08 0.02 0.26 1 IL 0.26 0.08 0.401 0.17 0.55 0.55 0.38 0.43 0.03 0.77 0.21 1

0 50 100 150 200 250 300 350

0.0

0.2

0.4

0.6

0.8

1.0

LAI (m2·m-2)

IL (imp·m-1)

LAI=0.0021*IL+0.137R2=0.409; (p<0.05)

Selection criteria: 1 interesting relationship; 2rational relationships; 3good correlations is expected.


0.0 0.5 1.0 1.5

0.0

0.2

0.4

0.6

0.8

1.0

CVM(m)

CHL (m)

CVM=0.672*CHL+0.008

R2=0.635; (p<0.05)

0.00 0.05 0.10 0.15 0.20 0.25

0.00

0.05

0.10

0.15

0.20

0.25

CVL(m3.m-1)

CVU (m3.m-1)

CVL=0.275*CVU-0.001

R2=0.566; (p<0.05)

vs.vs.

Results





DOSA

VIÑ

Ate

stAd

just

ed

appl

icat

ion

LIDA

R m

ap

Sens

ors

com

paris

on


Georeferenced LiDAR 3D Vine Plantation Map

Generation

Publication data: Sensors 2011, 11(6), 6237-6256

DOI: 10.3390/s110606237Impact factor 2010(JCR): 1.774Category: INSTRUMENTS & INSTRUMENTATIONQuartile: Q1

Journal: Sensors

Experimental work(LIDAR map)


LIDAR sensor

GPS sensor DGPS(EGNOS)


𝑋𝑋𝑖𝑖 = 𝑋𝑋𝑃𝑃 + 𝑐𝑐𝑐𝑐𝑐𝑐 𝛽𝛽 × 𝑑𝑑𝐿𝐿 × 𝑐𝑐𝑠𝑠𝑠𝑠 𝜃𝜃𝑖𝑖

𝑌𝑌𝑖𝑖 = 𝑌𝑌𝑃𝑃 + 𝑐𝑐𝑠𝑠𝑠𝑠 𝛽𝛽 × 𝑑𝑑𝐿𝐿 × 𝑐𝑐𝑠𝑠𝑠𝑠 𝜃𝜃𝑖𝑖

𝑍𝑍𝑖𝑖 = 𝑑𝑑𝐿𝐿 × 𝑐𝑐𝑐𝑐𝑐𝑐 𝜃𝜃𝑖𝑖

𝑋𝑋𝑃𝑃,𝑌𝑌𝑃𝑃

Data georeferencing procedure


Center of map (X,Y)

X dimension (m)

Y d

imen

sion

(m

)

Grid mapdimension Y (m)

Grid mapdimension X (m)

1 2 3 …

n

Z (m

)

Zmax

Zmin

Georeferenced density map

*.wmv


Coordinate X-UTM (m)

Coo

rdin

ate

Y-U

TM (m

)

0,0

LAI E

0,2

0,4

0,6

0,8

1,0

1,2

0 10 20 30 40

Total Canopy Partial Canopy (170 cm to 210 cm)Partial Canopy (130 cm to 170 cm)Partial Canopy (90 cm to 130 cm)Partial Canopy (50 cm to 90 cm)

Distance (m)





DOSA

VIÑ

Ate

stAd

just

ed

appl

icat

ion

LIDA

R m

ap

Sens

ors

com

paris

on


Variable rate dosing in precision viticulture: Use of

electronic devices to improve application

efficiency

Publication data : Volume 29, Issue 3, March 2010, Pages 239-248

DOI: 10.1016/j.cropro.2009.12.022Impact factor 2010(JCR): 1.517Cathegory: AGRONOMYQuartile: Q2

Journal: Crop Protection

Experimental work(Adjusted Application)


r/2

Ch

Ch/3

exs

Cw

r/2

Ch

Ch/3

exs

Cw

1/3 height (m)

Speed ct. (Km/h)

0.095 L/m3

sw xer (m)C −−=2

n

ivC

qh

u ×

××××=

60

10003

C (L/min)

w

Methodology of proportional application

Application coefficient:Byers et al., (1971)

Gil, (2001)Gil et al., (2001)



Ethernet (RJ-45)

PC CONTROLADVANTECH ARK 3384

COMPACT FIELDPOINT

National Instruments cFP-2120Software:

LABVIEW® 8.5

cFP-AI-100 cFP-AO-210

Signal 0-10 VCC

Signal 0-10 VCC

System configuration


Adaptació del Volum aplicat (mL) al Volum vegetació (m3)

0

1

2

3

4

5

6

7

8

50 52 54 56 58 60 62 64 66 68 70

Distància (m)

0.000

0.006

0.013

0.019

0.025

0.031

0.038

0.044

0.050

App

lied

volu

me

(mL/

10 c

m ro

w)

Canopy volum

e (m3)

Applied volume (mL) Variable rate

ResultsVariation of nozzle flow (mL) according crop structure measured (m3)

Distance (m)

Applied volume (mL) Conventional Canopy volume (m3)


Savings in applied volume.A

pplie

d vo

lum

e ra

te (L

/ha)

Variety, year and crop stage*

Conventional application Aplicació proporcional

Results

58 %

M-2007(85) Cs-2008 (75) Cs-2008 (85) T-2008(75) T-2008(85)

*According to BBCH classification.

Average

Variable rate application


Introduction

Objective

Experimental work


ConclusionsFONT: Balven


Conclusions (DOSAVIÑA Test)

We have described and presented the methodology of Dosaviña softwarevalidation in two distinct areas.

The result of using certified software allows its adaptation to differentsprayers and different crops structures.

The results obtained using the software Dosaviña can certify that it is asuccessful tool to adjust application volume.

For the use of this software requires a good calibration of sprayer used.


Conclusions (Sensors comparison)

Both LIDAR sensor and ultrasonic sensors are able to characterize vinesvegetation.

The LIDAR sensor obtains more detailed information, and with this we areable to extract more vegetative parameters. The ultrasonic sensor has theadvantage of simplicity in installation and data analysis .

The comparison of parameters determined by the sensors has enabledgood relationship between volume detected by two sensors and the leaf areamanually measured.

The use of these sensors, with the software for data process, can completeintelligent equipment in application of pesticides.


Conclusions (LIDAR map)

The method for georeferencing data obtained with the LIDAR sensorallows generation of digital maps.

The incorporation of a GPS has enabled sync information of both sides ofrow vegetation without the use of any point reference.

The improvement of the system should be based on synchronizationaccuracy of the two systems, the stability of the system and the adjustmenttilt of the LIDAR sensor.


Conclusions (Adjusted application)

The sprayer of proportional application is able to detect width changes invegetation and it can to fit an application resulting from these changesdetected.

The use of ultrasonic sensors at three different heights allow volumeadjustment more precise.

The adaptation of the application volume to the vegetation volume hasallowed good savings, more uniformity and deposition slightly higher.

The system of reading, registration, data analysis and actuation allows toinstall it in a conventional pesticide application equipment.


Funding of work

Projecte: OPTIDOSATítol: Reducció de l’ús de productes fitosanitaris en cultius arboris. Optimització de la dosid’aplicació en tractaments mecanitzats en vid.Referencia: AGL2007-66093-C04-02/AGRPeríode: 2007-2010Entitat finançadora: Ministerio de Ciencia e InnovaciónAltres participants: Universitat de Lleida, Universitat Politècnica de Valencia, InstitutoValenciano de Investigaciones AgrariasImport: 104.000 €

Projecte: SAFESPRAYTítol: Estratègies integrals per a una utilització de fitosanitaris segura i eficaç. Polvorització intel·ligent en vinya.Referència: AGL2010-22304-C04-04Període: 2011-2014Entitat finançadora: Ministerio de Ciencia e InnovaciónAltres participants: Universitat de Lleida, Universitat Politècnica de Valencia, InstitutoValenciano de Investigaciones AgrariasImport: 60.500 €

SECRETARÍA DE ESTADO DE INVESTIGACIÓN, DESARROLLO E INNOVACIÓN

Work realized in U.S.: Viticultural Consortium-EastLake Erie Regional Grape ProgramGrape Production Research Fund, New York Wine Grape Growers, and the New York Wine and Grape Foundation

SECRETARÍA DE ESTADO DE INVESTIGACIÓN, DESARROLLO E INNOVACIÓN


Thanks to:

Escola de viticultura i enologia Mercè Rossell i Domènech

Escola Tècnica Superior d’Enginyeria Agrària

Thank you for your attention

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