c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 3 ( 2 0 0 8 ) 282–293

avai lab le at www.sc iencedi rec t .com

journa l homepage: www.e lsev ier .com/ locate /compag

Verification of color vegetation indices for automatedcrop imaging applications

George E. Meyera,∗, Joao Camargo Netob

a Department of Biological Systems Engineering, 244 L.W. Chase Hall, University of Nebraska, Lincoln, NE 68583-0726, United Statesb Embrapa Information Tecnology, Av. Andre Tosello, 209, Cidade Universitaria “Zeferino Vaz”, PO Box 6041, Barao Geraldo,13083-886 Campinas, SP, Brazil

a r t i c l e i n f o

Article history:

Received 6 June 2006

Received in revised form

25 March 2008

Accepted 26 March 2008

Keywords:

Color images

Machine vision

Plant

Residue

Soil

Vegetation index

a b s t r a c t

An accurate vegetation index is required to identify plant biomass versus soil and residue

backgrounds for automated remote sensing and machine vision applications, plant ecolog-

ical assessments, precision crop management, and weed control. An improved vegetation

index, Excess Green minus Excess Red (ExG − ExR) was compared to the commonly used

Excess Green (ExG), and the normalized difference (NDI) indices. The latter two indices used

an Otsu threshold value to convert the index near-binary to a full-binary image. The indices

were tested with digital color image sets of single plants grown and taken in a greenhouse

and field images of young soybean plants. Vegetative index accuracies using a separation

quality factor algorithm were compared to hand-extracted plant regions of interest. A quality

factor of one represented a near perfect binary match of the computer extracted plant target

compared to the hand-extracted plant region. The ExG − ExR index had the highest quality

factor of 0.88 ± 0.12 for all three weeks and soil-residue backgrounds for the greenhouse set.

The ExG + Otsu and NDI − Otsu indices had similar but lower quality factors of 0.53 ± 0.39

and 0.54 ± 0.33 for the same sets, respectively. Field images of young soybeans against bare

soil gave quality factors for both ExG − ExR and ExG + Otsu around 0.88 ± 0.07. The quality

factor of NDI + Otsu using the same field images was 0.25 ± 0.08. The ExG − ExR index has a

fixed, built-in zero threshold, so it does not need Otsu or any user selected threshold value.

The ExG − ExR index worked especially well for fresh wheat straw backgrounds, where it

was generally 55% more accurate than the ExG + Otsu and NDI + Otsu indices. Once a binary

plant region of interest is identified with a vegetation index, other advanced image pro-

cessing operations may be applied, such as identification of plant species for strategic weed

control.

color vegetation indices utilize only the red, green and blue

1. Introduction

The use of vegetation indices in remote sensing of crop and

weed plants is not new. Studies for crop and weed detec-tion have been performed using different spectral bands andcombinations for vegetative indices (Woebbecke et al., 1995a;

∗ Corresponding author. Tel.: +1 402 4723377; fax: +1 402 4726338.E-mail address: gmeyer1@unl.edu (G.E. Meyer).

0168-1699/$ – see front matter © 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.compag.2008.03.009

© 2008 Elsevier B.V. All rights reserved.

El-Faki et al., 2000a,b; Marchant et al., 2001; Wang et al., 2001;Lamm et al., 2002; Mao et al., 2003; Yang et al., 2003). Some

spectral bands. The advantage of using color indices is thatthey accentuate a particular color such as plant greenness,which should be intuitive for human comparison. Woebbecke

a g r

ewdaa

C

w

r

aa

R

wbf

e

H

W(lwswL

aePa

N

Eftm(Mswsni

c o m p u t e r s a n d e l e c t r o n i c s i n

t al. (1995a) originally tested five color vegetation indices thatere derived using chromatic coordinates and modified hue toistinguish living plant material from bare soil, corn residue,nd wheat straw residue. Woebbecke’s indices without rownd column indices of each pixel included:

olor indices : (r − g, g − b,g − b

r − gand 2g − r − b) (1)

here r, g, and b were the chromatic coordinates.

= R∗

R∗ + G∗ + B∗ , g = G∗

R∗ + G∗ + B∗ , and b = B∗

R∗ + G∗ + B∗

nd, R*, G* and B* are the normalized RGB values (0–1) defineds:

∗ = R

Rm, G∗ = G

Gm, and B∗ = B

Bm

here R, G and B are the actual pixel values from the imagesased on each RGB channel and a sample of at least 100 pixelsrom each area of interest (plant, soil, or residue).

and Rm, Gm, and Bm = 255, are the maximum tonal value forach primary color.

Modified hue is derived from RGB values as:

ue = cos−1

[2R − G − B

2[R2 + G2 + B2 − RG − GB − RB

]1/2

](2)

oebbecke found that the excess green vegetation indexExG = 2g − r− b) provided a near-binary intensity image out-ining a plant region of interest. The plant regions of interestere then binarized using a selected threshold value for each

et of images. Woebbecke’s excess green (ExG) index has beenidely cited and used in recent studies (Giltelson et al., 2002;

amm et al., 2002; Mao et al., 2003; and others).Other color vegetation indices have been proposed to sep-

rate plants from soil and residue background images. Forxample, the normalized difference vegetation index (NDI) byerez et al. (2000) uses only green and red channels and is givens:

DI = G − R

G + R(3)

q. (3) is improved by adding a one, and then multiplying by aactor of 128. Hunt et al. (2005) used a similar index known ashe Normalized Green–Red Difference Index (NGRDI) for their

odel airplane photography for crop biomass. Gebhardt et al.2006) also used RGB transforms for their image segmentation.

ao et al. (2003) tested ExG, NDI, and the modified hue foreparating plant material from different backgrounds (soil andithered plant residue). In his study, the ExG index was found

uperior to the other methods tested. A critical step is usuallyeeded to select threshold value to binarize the index tonal

mage.

i c u l t u r e 6 3 ( 2 0 0 8 ) 282–293 283

Color indices have been suggested to be less sensitive to inlighting variations, and may have the potential to work wellfor different residues backgrounds (Campbell, 1996). A dis-proportionate amount of redness from various sources mayovercast a digital image, making it more difficult to iden-tify green plants with simple indices (Meyer et al., 2004b).For example, image redness may be related to digital cam-era operation and background illumination, but may alsobe related to redness from the soil and residue itself. Analternate excess red vegetative index (ExR = 1.4r − b) was pro-posed by Meyer et al. (1998a), but has not tested well in laterstudies.

Near-infrared (NIR) and color bands have been also usedwith vegetative indices for satellite remote sensing applica-tions. However, NIR is less human intuitive, since the humaneye is not sensitive to the NIR region. The human eye is onlyable to discern color, plants, and greenness from color images.NIR is not generally available with RGB color digital cameras.NIR usually requires a special monochromatic camera with anNIR filter. Another issue is how does one verify the accuracyof infrared-image-based vegetative index without compari-son to vegetation observed in a corresponding color visualimage?

If image vegetative/background classification is to be use-ful, the separated plant region of interest (ROI) must provideimportant canopy or leaf shape and venation texture fea-ture information to discriminate between broadleaf and grassspecies (Woebbecke et al., 1995b; Meyer et al., 1998a,b). Fourbasic steps for a computerized plant species classification sys-tem were presented by Camargo Neto (2004). The first stepis creating a binary image which accurately separates plantregions from background. This is the topic of this paper. Thesecond step is to use the binary image as a template to isolateindividual leaves as subimages using the original color plantpixels (Camargo Neto et al., 2006a). A third step was to applythe Elliptic Fourier shape feature analysis to each extractedleaf (Camargo Neto et al., 2006b). The fourth and final step wasto classify the plant species botanically using additional leafvenation textural features acquired during the previous steps(Camargo Neto and Meyer, 2005). An important application isthe mapping of weeds for site-specific field operations to makeherbicide applications more efficient and to reduce the totalamount of chemicals applied (Lindquist et al., 1998; Mortensenet al., 1992; Tillet et al., 2001; Wallinga et al., 1998; Zanin et al.,1998).

Two problems appear to exist with previous researchregarding vegetative indices (a) the disclosure of a man-ual or automatic threshold during the near-binary to binarystep, and (b) the lack of reporting of vegetation index accu-racy. Gebhardt et al. (2006) suggested that it is not necessaryto classify vegetation on a pixel basis with digital imaging.However, if there are too many plant pixels mixed up withbackground pixels, accuracy will be reduced. Hague et al.(2006) suggested a manual comparison of vegetative areasfrom high resolution photographs. To date, very few vege-tative index studies have reported the accuracy of detecting

plant material. The objective of this paper is to describe animproved color vegetation index with an automatic thresh-old and to determine its accuracy using plant-soil-residueimages.

i n a g

284 c o m p u t e r s a n d e l e c t r o n i c s

2. Materials and methods

2.1. Image acquisition

A set of color digital images of single plants of soybean(Glycine max (L.) Merrill), sunflower (Helianthus pumilus), redroot pigweed (Amarathus retroflexus), and velvetleaf (Abutilontheophrasti Medicus) were acquired after germination duringthe first three weeks of growth in an East Campus greenhouse,hereafter known as the Hindman set (Hindman, 2001). Threetypes of background were used: bare clay soil, weathered cornstalk, and fresh wheat straw residue. Images were acquiredusing a DC120 digital camera (Kodak Digital Science Rochester,NY) under natural sunlight at solar noon. The camera wasset to operate in the automatic mode and best quality imageresolution (1280 × 960 pixels) (Meyer et al., 2004a). The imagespatial resolution with this digital camera mounted one meterabove the plant targets translated into 0.5 mm per pixel. Usingthe Hindman set, 180 random digital images were selected andprocessed as five replications of each plant species, time afteremergence, and background.

A set of 40 images of young soybean (Glycine max (L.) Mer-rill) and weed plants (velvetleaf (Abutilon theophrasti Medicus),common sunflower (Helianthus annuus L.), and red pigweed(Amaranthus retroflexus L.)) were obtained during June 2003

Fig. 1 – Comparison of vegetative indices (ExG − E

r i c u l t u r e 6 3 ( 2 0 0 8 ) 282–293

at the USDA Management Systems Evaluation Area (MESA)field site at Shelton, NE. Plants were three weeks of age orless. These images are referred to as the Shelton set. Colorimages were taken with an Olympus E-10 (Olympus Imag-ing America Inc.), single lens reflex, digital camera, usingthe automatic mode. Image sets also included both wet anddry canopies taken near solar noon, with the sun light-ing behind the camera. Shadows were generally minimal.However in some cases, a nearby linear move system castsome shadows onto the plots. The image resolution was2240 × 1680 pixels, at 4 pixels per mm. The field soil (Platteloam) was mostly bare, with a few random corn stalks. Some ofthe leaves had insect damage by the third week. Three plantswere randomly selected from each image for vegetative indextests.

2.2. Vegetation index images

Vegetative index images were calculated for both Hindmanand Shelton images using MATLAB® script (The Mathworks,inc, Nattick, MA). Image sets A and B were used to assess

and compare the accuracy of vegetative indices: NDI, ExG,and ExG − ExR. The binary images of set A were generatedusing the Otsu threshold computed with the MATLAB ImageToolbox graythresh function for each NDI and ExG tonal index

xR, ExG, and NDI) and hand extracted mask.

c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 3 ( 2 0 0 8 ) 282–293 285

Fig. 2 – Examples of soybean plants with various backgrounds (a: bare soil, b: corn stalks, and c: wheat residue), respectivee exce

iEaae

Fp

xcess green (ExG) tonal images (d, e, and f), and respective

mage. The Otsu threshold method is described in Section 2.3.

xR was subtracted from ExG with a zero threshold to cre-te the ExG − ExR binary image. The bright tonal regions weressumed to represent candidate green plant regions of inter-st.

ig. 3 – Vegetative index value selection across a linearrofile shown in red using the mouse.

ss red (ExR) tonal images (g, h, and i).

For the Hindman set, binary reference images of plant pix-els (set B) were hand-generated and visually verified usingAdobe®Photoshop®5.0 LE for each color image. Examples ofthis process are shown in Fig. 1. The edge of each vegetativeregion was carefully outlined with a Wacom® tablet mouseand the Photoshop lasso tool. (This tablet mouse uses a freestyle electronic pen as a pointer.) The Lasso tool automaticallycloses upon completion of the selected area. All green plantpixels were then removed from the selected region. Next, theregion was changed to R = 255, G = 255, and B = 255 (or white).The remaining background pixels were set to zero (or black) toform a binary reference.

The binary Shelton set B represented a more formidablechallenge for manual plant pixel extraction, since there weretoo many plants (grasses and/or broadleaves) with complexand irregular regions of interest in these images. Conse-quently, three rectangular sub images with clearly, but simpleplant/background regions of approximately 200–1500 pixelswere cropped using the MATLAB imcrop. Template imagesof the plants were then interactively created with theMATLAB roipoly function, similar to the Photoshop lassotool.

2.3. Threshold method

Thresholding to obtain binary ExG and NDI index images (SetA) was performed using the method of Otsu (1979). Otsu’s

286 c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 3 ( 2 0 0 8 ) 282–293

Fig. 4 – Linear histogram index values for plant and bare soil background.

method is based on an analysis of the histogram of the tonalimage resulting from the initial vegetative index image cal-culation. A Gaussian filter was used to reduce noise in these

tonal images. Fig. 2 shows examples of the tonal or near-binaryimages obtained with ExG and ExR indices applied to differ-ent combinations of plant and backgrounds. The histogramswere assumed to be bimodal, representing two normal inten-

sity distributions, one representing plants and the remainderrepresenting the background. Otsu’s method provides an opti-mal index thresholding value (t) by maximizing the between

class tonal variance, while also minimizing the within classtonal variance of the image. Starting with a randomly selectedthreshold value t, all pixels that are equal to or below the valueof t belong to class C1; while all pixels greater than t belong to

c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 3 ( 2 0 0 8 ) 282–293 287

lues

tc

p

wr

Fig. 5 – Linear histogram index va

he other class C2. The probability of occurrence for each tonallass is defined as:

c1(t) =t∑

P(i) and pc2 =255∑

P(i) (4)

i=1 i=t+1

here pc1 and pc2 are the probabilities of classes C1 and C2,espectively.

for plant and corn stalk residue.

P(i) is the probability of tonal intensity ‘i’, defined as:

P(i) = hist(i)∑255k=1hist(k)

(5)

where hist(i) is the histogram frequencies of pixels withtonal intensity ‘i’, and k represents all of the possible tonalvalues.

i n a g

288 c o m p u t e r s a n d e l e c t r o n i c s

The between class tonal variance is given by:

�2B = pc1(t)[1 − pc1(t)][�1(t) − �2(t)]2 (6)

where

�1(t) =∑t

i=1iP(i)

pc1and �2(t) =

∑255i=t+1iP(i)

pc2

are the respective class variances.

Fig. 6 – Linear histogram index values

r i c u l t u r e 6 3 ( 2 0 0 8 ) 282–293

The weighted within-class tonal variance is next definedas:

�2w(t) = pc1(t)�2

1 (t) + pc2(t)�22 (t) (7)

where

�21 (t) =

t∑i

[i − �1(t)]2P(i)

pc1(t)and �2

2 (t) =t∑i

[i − �2(t)]2P(i)

pc2(t)

are the respective individual variances.

for plant and wheat straw residue.

a g r i c u l t u r e 6 3 ( 2 0 0 8 ) 282–293 289

tttEw

2

TpBnw

Q

w(Ogat

baAeratav

prPssd

3

3

ApnaPfiesltt

Table 1 – Analysis of variance: type 3 results for fixedeffects for factors affecting vegetative indices

Treatment effect DF Den DF F value Pr > Fd

ExG − ExRBacka 2 140 1.14 0.3229Plantb 3 1 19.88 0.163Timec 2 140 43.81 <0.0001Back × Plant 6 140 0.29 0.9406Back × Time 4 140 2.42 0.0516Plant × Time 6 140 9.97 <0.0001Back × Plant × Time 12 140 1.51 0.1283Rep 4 140 0.68 0.604

ExG + OtsuBack 2 140 60.79 <0.0001Plant 3 1 24.04 0.1486Time 2 140 40.46 <0.0001Back × Plant 6 140 4.84 0.0002Back × Time 4 140 6.4 <0.0001Plant × Time 6 140 5.42 <0.0001Back × Plant × Time 12 140 1.94 0.0347Rep 4 140 1.37 0.2492

NDI + OtsuBack 2 140 3.40 0.0363Plant 3 1 7.50 0.2607Timed 2 140 351.14 <0.0001Back × Plant 6 140 2.12 0.0545Back × Time 4 140 2.23 0.0691Plant × Time 6 140 1.34 0.2454Back × Plant × Time 12 40 2.54 0.0046Rep 4 140 1.75 0.1415

a Back = background: bare soil, corn stalks, wheat straw.

range of 0–100, and 300–450 along the linear axis with mouse.Plant pixels were visually identified from 100 to 300. In Fig. 4a,excess red (ExR) values would require an arbitrary positivethreshold value to binarize or separate the plant region from

c o m p u t e r s a n d e l e c t r o n i c s i n

In contrast to ExG or NDI with Otsu’s threshold method,he ExG − ExR vegetation index does not require a specialhreshold calculation. Plant pixel values are all positive, andhe remaining background pixels are all negative. Thus, thexG − ExR index is capable of self-generating a binary imageith a constant threshold of zero.

.4. Accuracy of vegetation indices

he accuracies of ExG − ExR, ExG, and NDI (Set A) were com-ared to the manually extracted binary vegetation images (Set). A quality factor Qseg defined by the Automatic Target Recog-ition Working Group (ATRWG) and Bhanu and Jones (1993)as used, given as:

seg =∑k,j=n,m

k,j=0 (A(i)k,j ∩ B(i)k,j)∑k,j=n,m

k,j=0 (A(i)k,j ∪ B(i)k,j)(8)

here A is the set of computer separated green plant pixelsi = 255) or background (i = 0) for ExGk,j with Otsu, NDIk,j withtsu, or ExG − ExRk,j, B is a reference set of manually separatedreen plant pixels (i = 255) or background (i = 0), k, j are the rownd column indices for the image, respectively and, n, m arehe image row and column sizes, respectively.

According to Eq. (8), vegetation separation accuracy isased on a logical or “∩” and a logical and “∪”, compared onpixel by pixel basis of target image B to the reference image. A separation quality Qseg of 1.0 represents a perfect indexxtraction of all selected class pixels, while Qseg near 0.0 rep-esents no class extraction in set B. The quality factor waspplied to each extracted plant of the Hindman set. However,he quality factor was selectively applied to cropped subim-ges of individual plants from the Shelton set that could beisually verified.

In order to test for significant differences in background,lant species, or time after emergence effects on plant sepa-ation quality Qseg, a statistical analysis was performed usingROC MIXED, SAS Analyst 9.3® (SAS Institute, Cary, NC). Aplit plot with repeated measures in time and a compoundymmetric covariance structure was used as the experimentalesign.

. Results and discussion

.1. Linear vegetative index comparisons

n initial comparison of vegetative index values for bothlant and background regions was conducted on the origi-al color images using the mouse and the MATLAB improfile,s shown in Fig. 3. The values for ExG, ExR, ExG − ExR, anderez’s red–green NDI indices along the profile are presentedor bare soil, corn stalk, and wheat straw residue, respectivelyn Figs. 4–6. A dashed horizontal zero line is included as a ref-rence between positive and negative values for each index

hown. The albedo of dry soils at Shelton (Central Platte Val-ey) was quite high, but exposure was adequately handled byhe automatic camera setting to produce visually good pic-ures.

b Plant = plant species: soybean, pigweed, sunflower, velvetleaf.c Time = week after emergence.d Level of Significance, � = 0.05.

Profile lengths varied slightly according to the number ofpixels selected by the mouse action taken. However, sufficientplant and background pixels were shown for comparison. ForFig. 4a and b, background pixels were visually identified over a

Fig. 7 – Overall mean quality with standard deviations forExG − ExR, ExG + Otsu, and NDI + Otsu vegetative indices.

290 c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 3 ( 2 0 0 8 ) 282–293

vegk int

Fig. 8 – Comparison of line histograms of NDI and ExG − ExRsample corn and bare soil/corn residue images at three-wee

the bare soil background. With excess green (ExG), a zerothreshold would separate most but not all of the plant regionfrom the background Fig. 4b shows ExG − ExR and normalizeddifference (NDI) index values over the same profile. ExG − ExRindex values (0–120) were almost the same as ExG for the plantregion. A zero threshold value for ExG − ExR would separatethe plant region from bare soil. The NDI values varied from125 to 150, and an arbitrary threshold line around 140 couldbe assumed to separate the plant from its background. NeitherExG nor NDI index values could separate plant and bare soilwith a zero threshold value.

Mouse selected index values for corn stalk residue areshown in Fig. 5. The difficulty shown in Fig. 5a is that the ExRand ExG index values are considerably more variable than for

bare soil values. The index values for background pixels werefound from 0 to 230 and from 590 to 900 along the profile axis.Plant index values were found from 100 to 300. The residueregions were characterized with considerably more redness

etative indices. Color and ExG − ExR binary images are forervals (Shelton Image Set).

(ExR: 5–80) than for bare soil. The ExG index values for residueranged from −30 to 0. ExG values were similar to bare soil.In Fig. 5b, the ExG − ExR index values remained entirely neg-ative in corn stalk residue region, but were quite positive forthe green plant region. The NDI was less definitive for cornstalk residue than for bare soil. Thus, ExG − ExR and perhapsExG could separate plant and corn stalk with a zero thresholdvalue.

Index values defined by mouse action are shown for freshwheat straw residue backgrounds in Fig. 6. Background regionswere found along the profile from 0 to 150, and from 400 to700. The plant region was found from 150 to 400. ExG indexresidue values varied considerably (−40 to 50), but were againpositive (20–80) in the plant region, as shown in Fig. 6a. The

ExR index values for wheat straw were positive (40–120), butvaried considerably (−10 to 10) across the zero line in theplant region. Neither ExG nor ExR would therefore discrimi-nate plant and background with a zero threshold. In Fig. 6b,

c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 3 ( 2 0 0 8 ) 282–293 291

Fig. 9 – Comparison of line histograms of NDI and ExG − ExR vegetative indices. Color and ExG − ExR binary images are forsample soybean, grass, and pigweed and dry and wet canopies and bare soil-corn and wheat fresh straw residue (SheltonI

tsvrtz

vvmt

3

VE

mage Set—2 p.m., June 19, 2003).

he ExG − ExR indices were negative (−50 to −145) for wheattraw regions but positive (5–80) for the plant region. The NDIalues for wheat straw varied from 0 to 120, while the plantegion values were around 150. Thus, ExG − ExR index was ableo separate plant and wheat straw regions with a nonarbitraryero threshold value.

Linear index profiles demonstrate typical vegetative indexalues and the random nature or noise that may occur forarious soil and residue systems. Threshold values must beanually selected or computed as an extra step to complete

he binaries of ExG and NDI tonal images.

.2. Comparison of indices—Hindman images

egetative indices of ExG − ExR with a fixed zero threshold andxG and NDI with Otsu thresholds were analyzed and com-

pared as a split plot, with repeated measure in time and com-pound symmetric covariance structure, as shown in Table 1.The dependent variable of interest for each image and indexwas the quality factor computed with Eq. (8). The basic factorsfound statistically significant at a p-value < 0.0001 were timefor ExG − ExR, time and background type for both ExG + Otsuand NDI + Otsu (at the 5% level). The background was lessstatistically important for NDI + Otsu than for ExG + Otsu.Apparently, ExG − ExR was not significantly affected by anyof the background types. Time seems to become a factor forall of these indices because by the second and third week, theplants were larger and occupied a larger area of the image

frame. A linear relationship in time by week was manifestedfor all background including the NDI + Otsu performance. Alinear relationship in time by week was also manifested forExG + Otsu for only corn stalks and wheat straw.

292 c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 3 ( 2 0 0 8 ) 282–293

Table 2 – Index quality factor results for grass, broadleaf, corn, soybean, and soil/corn residue backgrounds (SheltonImage Set)

Target type Target condition Quality factora

ExG − ExR ExG + Otsu NDI + Otsu

Soybeans Wet canopy 0.87 ± 0.06 0.88 ± 0.06 0.26 ± 0.08.88 ±

at Sharati

r

Soybeans Dry canopy 0

a 60 plants each indexed from digital images taken on June 11, 2003indicates near perfect separation. A value near 0 indicates poor sep

The final ExG − ExR binary images were generally veryclean as initially shown in Fig. 1. These images were visuallydifficult to differentiate from the hand-extracted templates.Both NDI + Otsu and ExG + Otsu binary images show numer-ous false positives (mistaken green plant pixels) in thebackground, especially with corn stalks and wheat straw.ExG + Otsu also showed many false positives of backgroundwithin each plant region of interest.

Fig. 7 summarizes the index quality means and stan-dard deviations for the soil-residue backgrounds for theentire three-week period for the Hindman set. The ExG − ExRalgorithm provided the best plant separation (0.80–0.93 aver-age index quality), more so than ExG − Otsu (0.21 ± 0.26to 0.85 ± 0.24) or NDI − Otsu 0.46 ± 0.37 to 0.67 ± 0.29 aver-age index quality). ExG − ExR results had smaller standarddeviations and were less sensitive to different soil-residuebackgrounds during the test period. The NDI + Otsu indexpresented good quality (0.93 ± 0.04) only for bare soil back-grounds during the third week and also during the thirdweek for corn stalk residue. NDI + Otsu index performancewas generally very poor during the first week for all back-grounds. Both ExG + Otsu and NDI + Otsu indices presentedpoor separation results (0.21 ± 0.26 to 0.67 ± 0.20) for allplants and wheat straw background during the entire timeperiod.

3.3. Comparison of indices—Shelton images

Figs. 8 and 9 show sample profiles for ExG − ExR and NDIapplied to Shelton color images and the binary imagesobtained using ExG − ExR and a zero threshold. The ExG − ExRindex appears to be quite accurate when applied to com-plex green corn seedlings of Fig. 8. The backgrounds werebare soil and corn residue. The NDI demonstrated onlymarginal success for plants and backgrounds with the samepixel information. Fig. 9 compares ExG − ExR with NDI forwet and dry soybean fields and soybeans with fresh wheatstraw applied. The soybean plants and grassy weeds wereseparated quite successfully from the background usingExG − ExR.

Both the ExG − ExR and ExG + Otsu indices identified plantregions in full sun more accurately (0.88 ± 0.06, 0.87 ± 0.04)than NDI + Otsu (0.26 ± 0.08), as shown in Table 2. Dry or wet

canopy and soil surfaces had little effect on separation perfor-mance. The NDI + Otsu index did not create enough contrastbetween plant and background for good separation perfor-mance for any of the field images tested.

0.08 0.87 ± 0.06 0.25 ± 0.08

elton, NE. Soybeans were at V2–V3 stages of growth. A value near 1on.

4. Conclusions and recommendations forfuture research

A fixed zero threshold, unsupervised vegetation index(ExG − ExR) was successfully tested using two commercialcolor digital cameras to separate plants and backgrounds forimage sets taken under greenhouse field lighting conditions.This index worked exceptionally well for single plants anddifferent soil-residue backgrounds. ExG − ExR also workedexceptionally well with natural lit color digital images. TheExG − ExR index showed superior vegetative separation accu-racy based on the ATRWG quality factor over the ExG + Otsuand NDI + Otsu indices for the Hindman set. However, bothExG − ExR and ExG − Otsu worked well for the bare soils in theCentral Platte Valley, at Shelton, NE. The NDI + Otsu vegetativeindex was shown to be unreliable for both image sets.

The manual extraction of a plant image template for com-parison with the vegetative index to determine accuracy istedious but quite necessary to determine accuracy. Addi-tional studies could help to determine the environmental andlighting factors that affect vegetative index accuracy. Thisapproach could be applied to near-infrared based vegetativeindices with a corresponding visual image.

The image vegetative index is an important critical step formachine vision identification of plant canopies in field cropsand weeds. Its primary purpose is to provide the boundaries ofthe plant regions of interest (a binary template) for subsequentdetailed feature analysis of plant properties. The resultingbinary template could be used to assist in the future extrac-tion of individual whole leaves for shape feature analysis andtextural assessment of venation for species characteristics.

Acknowledgements

The Agricultural Research Division (ARD), University ofNebraska-Lincoln, has approved this article as Journal SeriesNo. 15236. This work was supported in part by Embrapa, Camp-inas, Brasil and Nebraska ARD funds. We also thank Dr. JamesSchepers and the USDA MESA group for use of their field plots.Mention of specific trade names is for reference only and notto imply exclusion of others that may be suitable.

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