United States

Department of 

Agriculture

Forest Service

Northeastern 

Research Station

General Technical

Report NE-327

An Instructional Guide for 

Leaf Color Analysis using 

Digital Imaging Software

Paula F. Murakami

Michelle R. Turner

Abby K. van den Berg

Paul G. Schaberg
Published by: For additional copies:

USDA FOREST SERVICE USDA Forest Service 

11 CAMPUS BLVD SUITE 200 Publications Distribution

NEWTOWN SQUARE PA 19073-3294 359 Main Road

Delaware, OH 43015-8640

May 2005 Fax: (740)368-0152

Visit our homepage at: http://www.fs.fed.us/ne

Authors

PAULA F. MURAKAMI is a biological sciences laboratory technician and PAUL G. 

SCHABERG is a plant physiologist, USDA, Forest Service, Northeastern Research Station, 

705 Spear Street, South Burlington, VT 05403; MICHELLE R. TURNER is a research 

technician, The University of Vermont, Rubenstein School of Environment and Natural 

Resources, 80 Carrigan Drive, Burlington, VT 05405; and ABBY K. VAN DEN BERG is a 

research technician, The University of Vermont, Proctor Maple Research Center, P.O. Box 

233, Harvey Road, Underhill Center, VT 05490. 

Absbract

Changes in foliar color are a valuable indicator of plant nutrition and health. Leaf color is 

measured with visual scales and inexpensive plant color guides that are easy to use, but not 

quantitatively rigorous, or by employing sophisticated instrumentation including chlorophyll 

meters, reflectometers, and spectrophotometers that are costly and may require special 

training. Digital color analysis has become an increasingly popular and cost-effective method 

utilized by resource managers and scientists for evaluating foliar nutrition and health in 

response to environmental stresses. Working with colorful autumn samples of sugar maple 

(Acer saccharum Marsh.) leaves, we developed and tested a new method of digital image 

analysis that uses Scion Image or NIH image public domain software to quantify leaf color. 

This publication provides step-by-step instructions for using this software to measure the 

percentage green and red in leaves, colors of particular importance for the assessment 

of plant health. Comparisons of results from digital analyses of 326 scanned images of 

leaves and concurrent spectrophotometric measures of chlorophyll a, chlorophyll b, and 

anthocyanins verify that image analysis provides a reliable quantitative measure of leaf color 

and the relative concentrations of underlying plant pigments.
1

Introduction

Foliar color has been of great interest and value to resource managers and scientists as a visual 

indicator of plant health. Prior to the use of digital cameras, health assessments often relied on 

simple, visual scales to determine foliar color (Townsend and McIntosh 1993, Strimbeck 1997). 

Because this approach is somewhat subjective, charts depicting gradients of leaf color, including 

the Munsell Plant Tissue Color Chart (Sibley et al. 1995, Innes et al. 1996), the Globe Plant 

Color Guide, and the Leaf Color Chart were developed to help standardize color/assessments. 

These affordable guides are available for purchase and commonly are used to estimate foliar 

nitrogen status and evaluate seasonal changes in leaf color. More precise methods of foliar color 

analysis include those that measure chlorophyll content and spectral properties of leaves (i.e. 

reflectance- and transmittance- based measurements). Chlorophyll meters, spectrophotometers, 

reflectometers, and spectroradiometers are commonly used in the scientific community to 

evaluate plant physiological processes, such as leaf development (Woodall et al. 1998), leaf 

senescence (Lee et al. 2003, Feild et al. 2001, Merzlyak and Gitelson 1995, Boyer et al. 1988, 

Ishikura 1973), light absorption (Neill and Gould 1999), foliar nutrient status (Buscaglia and 

Varco 2002, Lopez-Cantarero et al. 1994, Lawanson et al. 1972), and fruit maturation (Reay 

et al. 1998). Pigment change due to environmental stresses, such as high light (Merzlyak and 

Chivkunova 2000), ultraviolet exposure (Dixon et al. 2001, Klaper et al. 1996, Burger and 

Edwards 1996), water stress (Ommen et al. 1999), and low temperature (Pietrini et al. 2002, 

Krol et al. 1995), can be measured using this sophisticated instrumentation. Advantages of 

this technology include greater accuracy of measurements and, in some cases, portability of 

equipment for field applications. However, this analysis also can be expensive and may require 

access to and training with specialized equipment, potentially hazardous chemicals, and other 

laboratory supplies.

Recently, digital imagery has become a new trend in plant color analysis. Digital cameras or 

scanners in combination with computers and appropriate software can be used to photograph, 

scan, and evaluate leaves for color with relative ease and at an affordable cost. In agriculture, 

digital technology has been used to characterize color in apples (Schrevens and Raeymaeckers 

1992), distinguish weeds from crops (Perez et al. 2000, Woebbecke et al. 1995, Zangh and 

Chaisattapagon 1995), identify storage-associated color change in chickory (Zhang et al. 2003) 

and apple (Vervaeke et al. 1994), and evaluate senescence rates in spring wheat (Adamsen et al. 

1999). An important application of digital image and color analysis has been in evaluating the 

influence of environmental stress on foliar health. Examples include water and nitrogen stress 

(Ahmed and Reid 1996), low temperature (Bacci et al. 1998), and disease, such as coffee leaf 

rust (Price et al. 1993), maize streak virus (Martin and Rybicki 1998), and powdery mildew 

infection of sweet cherry (Olmstead et al. 2001) and cucumber (Kampmann and Hansen 1994).

Research conducted by U.S. Department of Agriculture Forest Service and University of 

Vermont scientists has expedited the use of digital image analysis to evaluate seasonal color 

change in sugar maple (Acer saccharum Marsh.) leaves. One study found that sugar maple leaves 

with low nitrogen concentrations turned red earlier and more completely than those with high 

nitrogen concentrations (Schaberg et al. 2003). This group has devised an inexpensive method 

using a computer, scanner, and public domain software called Scion Image (Scion Corporation, 

www.scioncorp.com) or NIH Image (developed at the U.S. National Institute of Health and 

available at http://rsb.info.nih.gov/nih-image) to quantify green and red coloration in leaves. 
2

These software packages are free and can be easily downloaded from the internet.1 Development 

of Scion Image for PC users followed that of NIH image for Macintosh users. Both are widely 

used throughout the medical, scientific, and educational community.

This publication provides step-by-step instructions to use Scion Image software to measure 

the percentage of green and red in leaves as an indicator of plant nutrition and health. These 

instructions guide the reader through the process of computer-based leaf color analysis, 

including the scanning of leaves and the acquisition and use of image analysis software. 

Although the illustrations presented depict specific steps using Scion Image, these same 

instructions are applicable to analysis via NIH image. With practice, users will become 

proficient in a method for quantifying leaf color that requires no specialized equipment or 

chemicals.

Scanning Foliage

In the following example, leaves were scanned using an Epson Perfection 1200U flatbed 

scanner and Adobe Photoshop 5.0. A Power Macintosh G3 with OS 8.6 and a 17-inch Apple 

ColorSync Display were used to acquire, save, and analyze leaf images for percentage color. 

Other combinations of computers and scanners also can be used. Petioles were removed and 

leaves were placed adaxial (upper) side down on the scanner bed. After the lid was closed, leaves 

were scanned as a color photo at a resolution of 240 dpi and a scale of 100 percent. Images were 

saved as a Tagged Image File Format (TIFF).

Analysis and Quantification of Leaf Color

The following instructions guide the user through a detailed series of steps from launching 

Scion Image through calculating the percentage of green and red leaf areas for scanned images. 

In the example provided, a colorful sugar maple leaf is used for demonstration purposes. When 

the digital image of this leaf is first opened, two windows appear. One window shows a black 

and white version of the leaf. The other shows the original color version of the same leaf. These 

images are modified then combined for final color analysis. A glossary has been provided (page 

29) to define terms specific to Scion Image software. More information also can be obtained 

from the Scion Image website, www.scioncorp.com.

1

Scion Image for Windows is available for Windows 95/98/ME/NT/2000/XP. NIH image for Macintosh 

is available for system 7.0 or later. The use of trade or firm names in this publication is for the reader 

information and does not imply endorsement by the U.S. Department of Agriculture of any product or 

service.
3

Getting Started

1. Launch Scion Image. To open the first leaf image, choose File, then Open.

2. Select the file name for a leaf image and choose Open.

3. For each image, two windows appear—a black and white window and an Indexed Color 

window. These two windows are partially superimposed on each other.

Preparing the Black and White Image

4. Select the black and white window bringing it to the foreground. 
4

5. Go to Stacks2 and select Stack to Windows. Three windows are displayed showing the leaf 

image in shades of grey. 

2

Words in italics are defined in glossary on page 29.

6. Three windows appear labeled 001, 002, and 003, respectively. Window 003 is 

superimposed on window 002 and window 002 is superimposed on window 001. It is not 

necessary to see all three windows at the same time. 
5

7. Go to Windows and choose 001 bringing window 001 to the forefront. Close this 

window. Go to Windows again and choose 002 and bring window 002 to the forefront. 

Close this window. Only window 003 is visible now. DO NOT close this window.

8. With window 003 selected, go to Process. Choose Rank Filters.
6

9. A dialog box is displayed containing a list of options. Median (Reduce Noise) should 

already be selected along with Iteration of 1. Select OK.

10. Select Edit and then Invert. This reverses the white and black sections of the leaves on the 

screen.
7

11. Go to Options and then Threshold. This separates objects of interest from background 

based on grayscale.

12. Go to Process and then Binary and then Make Binary. At this point, processing of the 

black and white image is complete. Do not close the window because it is needed later.
8

Preparing the Color Image

13. To select the Indexed Color window, go to Windows and choose Indexed Color.

Removing Brown Areas Using the Paintbrush Tool

Areas of brown on the leaf caused by insect damage or necrosis should be removed so they are 

not measured as red. To remove the brown, proceed as follows:
9

Using the Paintbrush Tool

14. a. Click on the paintbrush tool ( ) from the Tools window found at the upper left of 

the screen. Move it into the Look-Up Table (LUT) window which can be found to the left 

of the Tools column. It turns into the eyedropper tool ( ). From the mosaic of colors 

shown in the LUT window, find a light gray or light blue and click on the color once. 

b. The paintbrush tool ( ) changes to the color chosen from the LUT window. The 

paintbrush can be resized to cover smaller or larger areas by double clicking on the tool 

and changing the pixel size.
10

Practicing with the Paintbrush Tool

c. Anywhere in the background, or “non-leaf” area of the Indexed Color window, click 

and hold while drawing a small test circle. Notice that the curser is now a circle when 

used within the Indexed Color window. 

d. Go to Stacks then 8-bit Color to RGB. 
11

e. Go to Stacks again then RGB to HSV.

f. Select OK to the warning message.
12

g. Look for the test circle and notice the color change. It should now be a shade of blue 

or yellow. If it is not blue or yellow, close the window and choose another color from the 

LUT window in step 14a and repeat steps 14b-14f until the test circle is blue or yellow.

h. Close the HSV window. Do not save. 
13

Masking Brown Areas

i. The paintbrush tool should still be selected. Click and hold to color over most brown 

spots or other areas that should be excluded from the color analysis. It may not be 

necessary to exclude very small areas of brown because their inclusion minimally alters 

the results of the color analysis. Use the hand tool ( ) to move within the window. 

Choose the hand tool, click in the window, hold and move. Click on the paintbrush 

tool again to resume coloring over brown regions. 
14

j. Some users prefer to save this image because coloring over brown areas can be very 

time consuming. By saving the image, the hassle of repeating preparatory steps can be 

eliminated should leaf color analysis on the same image be needed again. To save, go 

to File and then Save As. Assign the image a descriptive new file name such as: the old 

sample name with “brown removed” added, e.g., “56-red-2 brown removed”. 

Converting Color Image to Hue, Saturation, and Value Slices

15. With the new “brown removed”/Indexed Color window selected, go to Stacks and then 

8-bit Color to RGB. 
15

16. Go to Stacks and then RGB to HSV. 

17. Select OK to the warning that is displayed.
16

18. Go to Stacks again then Stack to Windows.

19. Three superimposed windows appear (labeled 001, 002, and 003). Close windows 002 

and 003. Only window 001 should be visible now. 
17

Combining Images Using the Image Calculator

20. Go to Process then Image Math.

21. Using the Image Calculator that appears, set up the equation exactly as shown below. Real 

Result should not be selected. Choose OK.
18

Viewing Indexed Color and Result Windows Simultaneously

22. Go to Windows and then Tile Images. This will reduce all open windows and display 

them simultaneously.

23. Extend the “brown removed” and Result windows by dragging the top or bottom of each 

window until they are adjacent.
19

Changing the Color of the LUT Bar

24. With the Result window selected, double click on the LUT tool ( ) in the Tools 

window. A solid red bar of color is displayed in the LUT window. This needs to be 

changed to a different color. Each time Scion Image is initially opened, this bar is red. 

25. To change the color of the solid bar, click once on the eyedropper tool ( ). 
20

26. Double click in the red bar and a Color window will be displayed. 

27. Select a new color to replace the red bar (purple works well). Choose OK.
21

28. Notice that the red bar is now purple. Click on the paintbrush tool and it, too, turns 

purple.

Aligning the Images

29. Be sure that the same areas of the leaf are visible in both the indexed color window and 

the Result window. If they are not similar, move the two images using the hand tool 

( ). Choose the hand tool, click in the window, hold and move. You should see the leaf 

image move within the window. Align the leaves within both windows. It is important to 

see the same areas of the leaf in order to successfully identify areas of color in each image. 

It is also helpful to see some blue or yellow regions which cover brown areas in the Result 

window to make sure they are not included in the analysis.
22

Measuring Green

30. Select the Result window.

31. Click on the LUT tool ( ). It may be necessary to double click on this tool to see the 

purple bar. Experiment with moving the top and bottom limits of the purple bar. Click on 

the top or bottom of the bar, hold and move up or down. Notice how it covers areas of the 

leaf in the Result window with purple. When the limits of the purple bar are dragged all 

the way to the top or bottom of the LUT window, click the top or bottom of the bar to 

“let go” of it, otherwise the bar will continue to move up or down as the cursor is moved.

32. To measure green, click in the middle of the purple bar and move the whole bar to the top 

of the LUT window. This will cover most of the leaf in the Result window with purple. 

Now move the bottom of the bar up or down until the whole leaf is purple, except for the 

brown areas which are blue or yellow.
23

33. Go to the top of the purple bar and move it down until you have covered only the 

green areas of the leaf. Compare with the green areas of the leaf in the “brown removed” 

window. Also, record the Lower number in the Info box in the bottom left-hand corner of 

the screen. In this example it is 13. This number is referred to again when measuring red. 

34. When all green areas of the leaf are highlighted by purple, it is time to measure. To do 

this, click Analyze and then Measure. 
24

Measuring Red

35. To measure red color, once again move the top part of the purple bar to the top of the 

LUT window. 

36. Move the bottom of the purple bar up until the red areas of the leaf are highlighted by 

purple. Observe the Upper number in the Info box; in this example it is 12. This number 

must be less than the Lower number recorded in step 33 to avoid measuring areas that 

have already been analyzed. 
25

37. Once again Analyze, then Measure. 

38. Sometimes red can also be measured in the lower region of the LUT bar, so it is good 

practice to move the purple bar all the way to the bottom of the LUT window. In doing 

so, look for additional red areas of the leaf to be covered by purple in the Result window. 

If red areas are highlighted by purple, then Analyze and Measure. Important: if measuring 

red in this region, avoid inadvertently measuring the brown areas. Before analyzing and 

measuring, move the top of the purple bar down until brown areas are blue or yellow, not 

purple. In this example there are no other red areas to be measured.
26

Measuring Total Leaf Area

39. When green and red have been measured, move the top and bottom of the purple bar so 

that the entire LUT window is purple. 

40. Click on Analyze and then Measure. This measures the total leaf area (including brown 

areas).
27

Viewing Results

41. To view actual values of measured leaf areas, choose Analyze and then Show Results. 

42. Record the values in the “Area” column for each color measured and the total leaf area. 

“1” is the green area, “2” is the red area and “3” is the total area. Make sure the combined 

green and red areas do not exceed the total area. If they do, then it is likely that the 

measured areas of green and red overlapped and will need to be remeasured. 

43. Using these numbers, calculate the percentage green and red area for each scanned leaf 

image.
28

Figure 1.—Linear and cubic relationships between percentage color and 

pigment (chlorophyll and anthocyanin) concentrations for 326 scanned 

images of sugar maples leaves.

R2 = 0.77; Green (%) = 14.69 + 5.09(chlorophyll a )

R2

 = 0.92; Green (%) = 52.44 + 4.17(chlorophyll a ) - 0.51(chlorophyll a - 9.11)2 +

0.02(chlorophyll a - 9.11)3

0

20

40

60

80

100

120

0 5 10 15 20 25

Chlorophyll a (�g cm-2)

Green (%)

R2 = 0.58; Green (%) = 25.53 + 9.77(chlorophyll b )

R2

 = 0.89; Green (%) = 54.96 + 11.85(chlorophyll b ) - 3.78(chlorophyll b - 3.64)2 +

 0.28(chlorophyll b - 3.64)3

0

20

40

60

80

100

120

0 2 4 6 8 10 12 14 16

Chlorophyll b (�g cm-2)

Green (%)

2

R2 = 0.72; Green (%) = 17.39 + 3.43(total chlorophyll)

R2

 = 0.92; Green (%) = 57.36 + 2.84(total chlorophyll) - 0.29(total chlorophyll - 12.75) + 

0.01(total chlorophyll - 12.75)3

0

20

40

60

80

100

120

0 5 10 15 20 25 30 35 40

Total chlorophyll (�g cm-2)

Green (%)

2

R2 = 0.72; Red (%) = 1.62 + 81.28(anthocyanin)

R2

 = 0.83; Red (%) = -1.64 + 153.65(anthocyanin) - 156.78(anthocyanin - 0.23) + 

53.66(anthocyanin - 0.23)3

0

20

40

60

80

100

120

0 0.5 1 1.5 2 2.5

Anthocyanins (�g cm-2)

Red (%)
29

Comparison of Percentage Color Versus Pigment Concentration

In order to assess the accuracy of using digital image color analysis as an indicator of foliar 

pigment concentrations, percentage leaf color and pigment content were determined for 326 

sugar maple leaf images. Fresh leaves were scanned and leaf disks were carefully removed. 

Punches were shredded using a razor blade and placed in either acetone/H2

O for extraction 

of chlorophylls or HCl/H2

O/MeOH for extraction of anthocyanin according to the methods 

of Gould et al. (2000). Pigment content was quantified using a Spectronic Genesys 8 

spectrophotometer (Cheshire, UK). Chlorophyll concentrations were calculated using the 

equations of Lichtenthaler and Wellburn (1983). Absorbance of anthocyanin was measured 

at 530 nm and the overlap of chlorophylls (0.24 x A653) was subtracted (Murray and Hackett 

1991). Regression analyses were conducted to determine the relationship between percentage 

leaf color and pigment concentration using the least squares method (SAS 2002) (Fig. 1). Linear 

regressions for each color were significant at P<0.05. However, because entirely green or red 

leaves could only be measured as 100 percent green or red regardless of how much chlorophyll 

or anthocyanin accumulated, a saturation in percentage color was sometimes observed. Due to 

this saturation effect, associations between percentage color and pigment concentration were 

better accounted for by polynomial regressions (Fig. 1).

Conclusion

Analyses with sugar maple leaves indicate that digital image analysis provides an accurate 

means of quantifying foliar color and estimating pigment concentration in multicolored leaves. 

Although this method has value, it is not intended to replace other more precise means of 

measuring color. However, it does provide resource managers and scientists with an alternative 

means of quantifying leaf color and aspects of plant health that do not require specialized 

equipment or potentially hazardous chemicals.

Glossary

Note: a more detailed description of terms and specific functions can be found in the User 

Manuals on the Scion Corporation website: www.scioncorp.com.

8-bit Color to RGB: creates a stack with three slices (red, green, blue) from an 8-bit indexed 

color image.

Binary, Make Binary: creates a binary (black and white) image from a grayscale image.

Image Math: executes an arithmetic operation between selected images.

Invert: inverts pixel values.

Look-up Table (LUT): all images have a concurrent look-up table which displays pixel values 

(0-255) as colors.

Rank Filter (Median): reduces noise by replacing all 9 pixels in a “3 x 3 neighborhood” with 

the median value of that neighborhood.
30

RGB to HSV: creates a HSV (hue, saturation, value) image from a RGB image.

Stack: a three-dimensional image comprised of two or more slices. 

Stack to Windows: converts a stack into windows which correspond to the individual slices of 

that stack.

Threshold: displays objects of interest and background based on gray level. Background pixels 

are ignored when measuring area.

Acknowledgments

The authors would like to thank Heather Heitz, Kelly Baggett, Charlotte Jasko, Louanne 

Collins, and Sam Nijensohn for reviewing the instructional portion of this document. We 

would also like to thank Jan van den Berg and Rick Strimbeck for their technical advice on the 

development of the digital color analysis. A special thank you to John Shane, Dr. Samuel Neill, 

and Dr. Stephen Saupe for providing helpful editorial suggestions during final review of this 

manuscript.

Literature Cited

Adamsen, F.J.; Pinter, Jr., P.J.; Barnes E.M. [et al.]. 1999. Measuring wheat senescence with a 

digital camera. Crop Science. 39(3): 719-724.

Ahmad, I.S.; Reid, J.F. 1996. Evaluation of colour representations for maize images. Journal 

of Agricultural Engineering Research. 63(3): 185-196.

Bacci, L.; De Vincenzi, M.; Rapi, B. [et al.]. 1998. Two methods for the analysis of 

colorimetric components applied to plant stress monitoring. Computers and Electronics 

in Agriculture. 19(2): 167-186.

Boyer, M.; Miller, J.; Belanger, M.; Hare, E. 1988. Senescence and spectral reflectance 

in leaves of Northern pin oak (Quercus palustris Muenchh.). Remote Sensing of 

Environment. 25(1): 71-87.

Burger, J.; Edwards, G.E. 1996. Photosynthetic efficiency, and photodamage by UV and 

visible radiation, in red versus green leaf coleus varieties. Plant Cell Physiology. 37(3): 

395-399.

Buscaglia, H.J.; Varco, J.J. 2002. Early detection of cotton leaf nitrogen status using 

reflectance. Journal of Plant Nutrition. 25(9): 2067-2080.

Dixon, P.; Weinig, C.; Schmitt, J. 2001. Susceptibility to UV damage in Impatiens capensis

(Balsaminaceae): testing for opportunity costs to shade-avoidance and population 

differentiation. American Journal of Botany. 88(8): 1401-1408.

Feild, T.S.; Lee, D.W.; Holbrook, N.M. 2001. Why leaves turn red in autumn. The role of 

anthocyanins in senescing leaves of red-osier dogwood. Plant Physiology. 127(2): 566-574.
31

Gould, K.S.; Markham, K.R.; Smith, R.H.; Goris, J.J. 2000. Functional role of anthocyanins 

in the leaves of Quintinia serrata A. Cunn. Journal of Experimental Botany. 51(347): 

1107-1115.

Innes, J.L.; Ghosh, S.; Schwyzer, A. 1996. A method for the identification of trees with 

unusually color foliage. Canadian Journal of Forest Research. 26(9): 1548-1555.

Ishikura, N. 1973. The changes in anthocyanin and chlorophyll content during the 

autumnal reddening of leaves. Kumamoto Journal of Science Biology. 11(2): 43-50.

Kampmann, H.H.; Hansen, O.B. 1994. Using colour image analysis for quantitative 

assessement of powdery mildew on cucumber. Euphytica. 79(1/2): 19-27.

Klaper, R.; Frankel, S.; Berenbaum, M.R. 1996. Anthocyanin content and UVB sensitivity in 

Brassica rapa. Photochemistry and Photobiology. 63(6): 811-813.

Krol, M.; Gray, G.R.; Hurry, V.M [et al.]. 1995. Low-temperature stress and photoperiod 

affect an increased tolerance to photoinhibition in Pinus banksiana seedlings. Canadian 

Journal of Botany. 73(8): 1119-1127.

Lawanson, A.O.; Akindele, B.B.; Fasalojo, P.B.; Akpe, B.L. 1972. Time-course of anthocyanin 

formation during deficiencies of nitrogen, phosphorus and potassium in seedlings of 

Zea mays Linn. Var E.S. I. Zeitschrift fur Pflanzenphysiologie. 66(3): 251-253.

Lee, D.W.; O’Keefe, J.; Holbrook, N.M.; Field, T.S. 2003. Pigment dynamics and autumn 

leaf senescence in a New England deciduous forest, eastern USA. Ecological Research. 

18(6): 677-694.

Lichtenthaler, H.K. ; Wellburn, A.R. 1983. Determinations of total carotenoids and 

chlorophylls a and b of leaf extracts in different solvents. Biochemical Society 

Transactions. 603: 591-592.

Lopez-Cantarero, I.; Lorente, F.A.; Romero, L. 1994. Are chlorophylls good indicators of 

nitrogen and phosphorus levels? Journal of Plant Nutrition. 17(6): 979-990.

Martin, D.P.; Rybicki, E.P. 1998. Microcomputer-based quantification of maize streak virus 

symptoms in Zea mays. Phytopathology. 88(5): 422-427.

Merzlyak, M.N.; Gitelson, A. 1995. Why and what for the leaves are yellow in autumn? On 

the interpretation of optical spectra of senescing leaves (Acer platanoides L.). Journal of 

Plant Physiology. 145(3): 315-320.

Merzlyak, M.N.; Chivkunova, O.B. 2000. Light-stress-induced pigment changes and 

evidence for anthocyanin photoprotection in apples. Journal of Photochemistry and 

Photobiology B: Biology. 55(2/3): 155-163.
32

Murray, J.R.; Hackett, W.P. 1991. Dihydroflavanol reductase activity in relation to 

differential anthocyanin accumulation in juvenile and mature phase Hedera helix L.

Plant Physiology. 97(1): 343-351.

Neill, S.; Gould, K.S. 1999. Optical properties of leaves in relation to anthocyanin 

concentration and distribution. Canadian Journal of Botany. 77(12): 1777-1782.

Olmstead, J.W.; Lang, G.A. 2001. Assessment of severity of powdery mildew infection of 

sweet cherry leaves by digital image analysis. HortScience. 36(1): 107-111.

Ommen, O.E.; Donnelly, A.; Vanhoutvin, S. [et al.]. 1999. Chlorophyll content of spring 

wheat flag leaves grown under elevated CO2 concentrations and other environmental 

stresses within the ‘ESPACE-wheat’ project. European Journal of Agronomy. 10(3/4): 197-

203.

Perez, A.J.; Lopez, F.; Benlloch, J.V.; Christensen, S. 2000. Colour and shape analysis 

techniques for weed detection in cereal fields. Computers and Electronics in Agriculture. 

25(3): 197-212.

Pietrini, F.; Iannelli, M.A.; Massacci, A. 2002. Anthocyanin accumulation in the illuminated 

surface of maize leaves enhances protection from photo-inhibitory risks at low 

temperature, without further limitation to photosynthesis. Plant, Cell and Environment. 

25(10): 1251-1259.

Price, T.V.; Gross, R.; Ho Wey, J.; Osborne, C.F. 1993. A comparison of visual and digital 

image-processing methods in quantifying the severity of coffee leaf rust (Hemileia 

vastatrix). Australian Journal of Experimental Agriculture. 33(1): 97-101.

Reay, P.F.; Fletcher, R.H.; Thomas, V.J. 1998. Chlorophylls, carotenoids and anthocyanin 

concentrations in the skin of ‘Gala’ apples during maturation and the influence of foliar 

applications of nitrogen and magnesium. Journal of the Science of Food and Agriculture. 

76(1): 63-71.

SAS Institute, Inc. 2002. JMP: The Statistical Discovery Software, Version 5. Cary, NC.

Schaberg, P.G.; van den Berg, A.K.; Murakami, P.F. [et al.]. 2003. Factors influencing red 

expression in autumn foliage of sugar maple trees. Tree Physiology. 23(5): 325-333.

Schrevens, E.; Raeymaeckers, L. 1992. Colour characterization of golden delicious apples 

using digital image processing. Acta Horticulturae. 304: 159-166.

Sibley, J.L.; Eakes, D.J.; Gilliam, C.H. [et al.]. 1995. Growth and fall color of red maple 

selections in the southeastern United States. Journal of Environmental Horticulture. 

13(1): 51-53.

Strimbeck, G.R. 1997. Cold tolerance and winter injury of montane red spruce. Burlington, 

VT: The University of Vermont. 124 p. Ph.D. dissertation.
33

Townsend, A.M.; McIntosh, M.S. 1993. Variation among full-sib progenies of red maple in 

growth, autumn leaf color, and leafhopper injury. Journal of Environmental Horticulture. 

11(2): 72-75.

Vervaeke, F.; Schrevens, E.; Verreydt, J. [et al.]. 1994. The use of digitized video images for 

monitoring color and color evolution of Jonagold apples during shelf life. In: Yano, 

T.; Matsuno, R.; Nakamura, K., eds. Proceedings of the sixth international congress on 

engineering and food; 1993 May 23-27; Chiba, Japan. London, UK: Blackie Academic and 

Professional: 200-202.

Woebbecke, D.M.; Meyer, G.E; Von Bargen, K.; Mortensen, D.A. 1995. Color indices for 

weed identification under various soil, residue, and lighting conditions. Transactions of 

the ASAE. 38(1): 259-269.

Woodall, G.S.; Dodd, I.C.; Stewart, G.R. 1998. Contrasting leaf development within the 

genus Syzygium. Journal of Experimental Botany. 49(318): 79-87.

Zhang, N.; Chaisattapagon, C. 1995. Effective criteria for weed identification in wheat 

fields using machine vision. Transactions of the ASA. 38(3): 965-974.

Zhang, M.; De Baerdemaeker, J.; Schrevens, E. 2003. Effects of different varieties and 

shelf storage conditions of chicory on deteriorative color changes using digital image 

processing and analysis. Food Research International. 36(7): 669-676.
Printed on Recycled Paper

Murakami, Paula, F.; Turner, Michelle, R.; van den Berg, Abby, K.; Schaberg, Paul, 

G. 2005. An instructional guide for leaf color analysis using digital imaging 

software. Gen. Tech. Rep. NE-327. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Research Station. 33 p.

Digital color analysis has become an increasingly popular and cost-effective method 

utilized by resource managers and scientists for evaluating foliar nutrition and health 

in response to environmental stresses. We developed and tested a new method of 

digital image analysis that uses Scion Image or NIH image public domain software 

to quantify leaf color. This publication provides instructions for using this software to 

measure the percentage green and red in leaves, colors of particular importance for 

the assessment of plant health. Comparisons of results from digital analyses of 326 

scanned images of leaves and concurrent spectrophotometric measures of 

chlorophyll a, chlorophyll b, and anthocyanins verify that image analysis provides a 

reliable quantitative measure of leaf color and the relative concentrations of 

underlying plant pigments.

Keywords: chlorophyll, anthocyanin, leaf color, digital image, image analysis, Scion 

Image software, NIH Image software
Headquarters of the Northeastern Research Station is in Newtown Square, 

Pennsylvania. Field laboratories are maintained at:

Amherst, Massachusetts, in cooperation with the University of Massachusetts

Burlington, Vermont, in cooperation with the University of Vermont

Delaware, Ohio

Durham, New Hampshire, in cooperation with the University of New Hampshire

Hamden, Connecticut, in cooperation with Yale University

Morgantown, West Virginia, in cooperation with West Virginia University

Parsons, West Virginia

Princeton, West Virginia

Syracuse, New York, in cooperation with the State University of New York, 

College of Environmental Sciences and Forestry at Syracuse University

Warren, Pennsylvania

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