Precision agriculture in cotton:Definition of the optimal imaging

resolution required for purple nutsedge detection

Tal Miller, Liraz Cohen, Eldar Peleg, Matan Gilad and Anat Stein

Western Galilee Regional Highschool

Hanan EizenbergDepartment of Weed Research,

Newe Ya’ar Research Center, Agricultural Research Organization (ARO)

The 2nd International Conference of Novel and Sustainable Weed Management in Arid and Semi-

Arid Agro EcosystemsSeptember 6th-10th , Santorini, Greece

Introduction• Purple nutsedge is a troublesome weed, causing

severe damage in cotton• Weeds may compete on resources such as water,

light, space, nutrients etc.

The effect of purple nutsedge infestation on cotton biomass

y = -0.2008x + 41.577R2 = 0.908

0

5

10

15

20

25

30

35

0 50 100 150 200

משקל יבש של עשבי גומא הפקעים )גרם(

ם(גר

ה )תנ

הכוחי

צמשל

ש יב

קל מש

Purple nutsedge biomass (g m-2)

Cotto

n bi

omas

s (g

m-2)

Precision agriculture approach

• Precision agriculture, specifically, site specific weed management is a modern approach for reducing herbicide rates

• This could be achieved by spraying herbicides only on weed patches based on the detection of the spatial distribution of weeds (and not on the entire field)

50% savings

50% savings

66% savings

• Weeds are easily detected visually because they are green plants on brown soil

• Several indexes were developed for this purpose

How can we detect weeds grown in the field?

Normalized Difference Vegetation index (NDVI)

)()(

redNIRredNIRNDVI

• NIR = Reflection in Near Infra Red (770hm)• Red = Reflection in Red (660hm)

• It was reported in the literature that NDVI is highly correlated to vegetative growth, nitrogen and chlorophyll levels

NDVI image by NASA

NDVI of wheat field pre planting

savings 50%

Wheat field pre planting

When NIR channel is not available, NGRDI index may be used:

)()(

redGreenredGreenNGRDI

• The main goal of this study was to determine the optimal resolution required for the detection of purple nutsedge in cotton

Research objectives

Specific objectives were:• Detecting purple nutsedge

on bare soil (inter rows) using NGRDI index

• Defining the threshold resolution for purple nutsedge detection

Research objectives (cont.)

High resolution RGB image

(0.05 x 0.05 m per pixel)

Hypothesis

We hypothesize that NGRDI values, greater than bare soil NGRDI (~0.01) represent vegetative growth, in our case purple nutsedge infestation

• Experiments were performed in a commercial cotton field in the Jesreel Valley, Northern Israel

• Aerial images were captured at the same day of data collection

• Image resolution was 0.05 x 0.05 m per pixel• Twenty-five plots were randomly selected for data

collection

Materials & Methods

Materials & Methods (cont.)• Weed coverage (%) was visually estimated• Purple nutsedge shoots were counted• Plot locations were marked using a differential GPS

(dGPS – sub-meter)• Data were imported into a Geographical Information

Software (GIS) software for advance analysis

Results

10m

Data processing

• Originally pixel size was 0.05 x 0.05 m• Computing values of RGB channels• Reducing the resolution by increasing the size of the

pixels• Re-computing values for the merged pixels by using

the average value

0.45 m

1.70 m

Imaging

GIS

ObservationsFixing imageMarking

location by dGPS

Increasing pixel size

Validation

Determining threshold value

No Yes

Creating multi-layer mapChoosing

plotsColor channels analysisComputing NGRDI

indexIs index value higher than ground value?

Relations between pixel size and NGRDI index

0 1000 2000 3000 4000

NGRD

I

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

Pixel size (cm2)

Conclusions• The threshold resolution for purple nutsedge

detection from an aerial RGB image is 0.5 X 0.5 m per pixel (using NGRDI index)

• Although NIR imaging is separating better weed from soil, using RGB channels (NGRDI index) is much cheaper and available for weed detection

• Weed coverage that causes damage to cotton could be detected with a resolution of 0.5 x 0.5 m per pixel

Acknowledgment• EWRS for supporting my trip• Anat Stein for her assistance and motivation• Research team in the Newe Ya’ar Research Center,

Department of Weed Research• Dr. Yafit Cohen, Sensing, Information and

Mechanization Engineering, ARO• Jimmie Ipen, field crops action, Alonim• Shay Mey-tal, Agam LTD• The school, for support and resources

Thank you for your attention!