# Evaluation of Image Analysis to Determine

Authors: Simone Graeff; Judit Pfenning; Wilhelm Claupein; Hans-Peter Liebig
Journal: Advances in Optical Technologies
 (2008)

---

## Abstract

Numerous models have been developed for calculating optimum decision rules for nitrogen fertilization based on remote sensing techniques. New technologies related to digital image analysis may provide an alternative method to estimate nutrient status faster and more efficiently than current techniques. A series of field studies was conducted to determine the applicability of digital image analysis for nitrogen demand estimates in broccoli plants. Digital images were taken under constant light conditions in various wavelength ranges (380–1300 nm) using a digital imager. Images were processed for the parametersa∗ and b∗ in the L∗a∗b∗ color system. The image analysis showed a close correlation between the nitrogen status of broccoli plants and the parameter b∗ of the L∗a∗b∗ color system especially in the wavelength ranges 510780 and 516780 nm. The relationship between nutrient concentration in leaf dry matter and the parameters b∗ was used to determine the N fertilizer demand within the cultivation period. Estimated N amounts were applied as top dressing four weeks after setting and were 100 kg ha−1 lower than the standard fertilizer rate. Calculated N balances indicated a total uptake of applied N amounts in the image-based N treatments without considerable yield loss. Thus, digital image analysis proved to be an effective means of determining nitrogen status and adjusting fertilizer applications to preserve or enhance crop quality and yield considering sustainability.


---

## Body

## 1. Introduction

Accurate  prediction of
nutrient requirements of plants during the cultivation period is necessary for
efficient fertilizer use [1]. Soil and plant analyses are currently the basic
techniques to predict the nutrient status of plants in the field. However,
these techniques are time-consuming, plant-destructive, and labor-intensive.Nitrogen (N) is the
nutrient most often applied to vegetable crops as a fertilizer. One strategy
for pursuing profitable yields and a high quality in vegetable production while
avoiding at the same time water contamination is to only apply the amounts of N
that will be used by the growing crop. The ability to quickly and easily
monitor the nutrient demand in vegetables and to use that information to
optimize N fertilizer inputs would directly contribute to improved crop quality
and resource use efficiency.In the last few years,
low-cost digital imaging methods have become readily available easing the
set-up of calibrations for the detection of different plant stresses. Image-based
methods are fast once fully developed, and they have the added advantage that images
can be acquired quickly for different times to assess vegetation dynamics for
monitoring purposes. In addition, images are easily archived; thus, conserving
maximal data for future change analyses. Interpretation of digital images is
aided by computerized processing of color parameters. Color parameters can be
evaluated in different color systems. The RGB or the CIELAB color values have
been mostly used to describe and analyze color images [2].However, research in
that area mainly focused on image analysis techniques for measuring and
estimating vegetation cover and/or bare soil
[3–6]. Digital image analysis
has been used in several studies to quantify, for example, disease, stress, coverage,
and color [5, 7–9]. These studies concluded that digital image analysis is very
useful to quantify biophysical plant parameters. Images have also been used to
estimate tiller densities across a field [10], to detect insect damage [11], as
well as nutrient and water deficiencies [12, 13]. Up to now, a number of
different stresses are detectable through images, but the pattern of specific
stresses needs to be verified by controlled observations including known
reference conditions within the image.Changes in foliar color are a valuable indicator of plant nutrition and
health. Leaf color can be 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. Reflectance
data gathered with reflectometers or spectrophotometers can provide accurate and
precise quantification of stress factors in plants [14–16]. Spectral indices derived from reflectance data or the
single use of reflectance data have shown to be indirectly related to the
nitrogen status of crops, (e.g., [17–22]). Although reflectance of
a crop depends on many factors, including incident lighting and physiological
condition of the crop [23], nutrient deficiencies can be detected by
calibrating photographic responses within fields against responses from areas
measured as nonlimiting in nutrients [12]. The assessment of the nutrient
status using digital images relies on structural changes of leaf tissue.
Disorientated tissue results in reduced light reflectance leading to a change
of color parameters. Thus, it is desirable to capture color information of
plants in color images and to analyze the images. However, most of these developed
systems and calibrations have been widely tested in arable crops. Suitable
calibrations for vegetables are widely missing.Working with broccoli
plants supplied with different N rates, we developed and tested a method of
digital image analysis to determine necessary N fertilizer rates based on
actual Nstatus. This research is intended to develop an image-processing
method to  assess broccoli nitrogen level based on digital images, split into
several wavelength ranges by the application of different long-pass filters. The
applied method converted the image into the LAB color system [24]. In 1986, the
Commission Internationale d'Eclairage (CIE) updated and refined the three-dimensional Lab
color system, which displays every color perceived by the human eye (CIE 1976). Unlike RGB colors, CIE Lab colors are device-independent. Therefore, the
visual characteristics of these colors remain consistent on monitors, printers
and scanners. Colors are defined by the L*, a*, and b* parameters. Lightness is
expressed as “L” value from dark to light with 0 = black and 100 = white. The
“a” and “b” values indicate the chromaticity coordinates.
Green to red is expressed by the “a” value. A more positive value
represents red, while a more negative value represents green. Blue to yellow is
expressed by the “b” value. A more positive value represents yellow,
a more negative value represents blue. a* and b* color parameters representing
the reflectance of the broccoli plant across leaves were used for the
discrimination of nitrogen status. The obtained data was further processed to
find specific patterns in selected wavelength ranges that correspond to crop
nitrogen status. To implement a wavelength analysis, the digital image was recorded
in multiple wavelength ranges in the visible and near-infrared spectra and
diverted into packets of narrow frequency bands to find the lowest-level
approximations at different levels. The maximum coefficients were identified
for interested signal bands and then compared to chemically determined nitrogen
concentrations in broccoli leaves as the ground-truth nitrogen level. Thus,
this study explores an innovative image-processing method to assess broccoli
nitrogen status based on digital images. The paper describes the process and the
validation of this pioneering research in vegetables.


## 2. Material and Methods

### 2.1. Field Experiments

Field studies were conducted at the experimental station
of horticulture (697 mm average annual precipitation; 8.8°C mean
annual temperature), of the University of Hohenheim, Stuttgart,
Germany.
The first field trial consisted of two common broccoli cultivars “Parthenon”
and “Marathon” and four N fertilization
treatments consisting of 0, 100, 200, and 400 kg N
ha-1. The total amount of nitrogen was split in two rates in the 400 kg treatment, broadcast and incorporated in all treatments by hand as KAS
(13.5% NH4-N, 13.5% NO3-N, 12% CaO). This
field trial was used to set up to derive a relationship between spectral
properties of broccoli plants and nitrogen demand and to establish the
necessary calibrations for the estimation of N fertilizer amounts.In a second field trial the same varieties were tested
under six different N fertilization treatments consisting of a final
amount of 0, 76, 152, 200, 248 and 304 kg N ha-1. The total
amount of nitrogen was split in two rates, whereof 50% was applied in the
first rate at setting, broadcast and incorporated in the treatments up to 304 kg ha-1 as KAS (13.5% NH4-N, 13.5% NO3-N,
12% CaO) and in the 248 kg ha-1 treatment as ENTEC26
applied at setting. The amount of the second nitrogen rate was calculated based
on the N demand of broccoli plants derived out of the digital image analysis
and thus to validate the derived calibrations.Broccoli was planted in both trials on July 05th in 06
at a rate of 4.5 plants per m2. The trial set
up was a randomized block design with four replications. Timing of N
application was set to start of cultivation, that is, at setting and after a
four-week growing period (before beginning of floret formation while period of
leaf formation). Soil Nmin content was determined in all plots at
planting and after final harvest to calculate an N balance for each treatment. Broccoli
plants were irrigated to ensure a nonlimiting water supply throughout the main
growing period. Soil nutrient status analyses were made at the beginning of
vegetation period to avoid any other nutrient deficiencies in broccoli other
than nitrogen. Plant protection measures were carried out as needed to control
weeds and pests. Harvest of broccoli florets started in the mid of September as
soon as 50% of the plants of a plot had reached a minimum marketable head
size of ∅7 cm or tended to show further flower
development. Two harvests were accomplished in a four-day interval. Florets
were trimmed to market specifications for broccoli and graded for diameter,
weight, discoloration, and hollow stem.


### 2.2. Image Acquisition

Image acquisition started two weeks after planting and
was carried out once a week on the youngest fully developed leaf of each plant
until final harvest of broccoli florets. Images were taken with a digital,
light-sensitive (ISO 200–2400; spectral
sensitivity of 250–1300 nm),
high-spatial resolution (5140*5140 pixel) imager (S1 PRO, Leica, Germany).
Settings of the imager were chosen according to Graeff [25]. The imager
provides an entirely digital and fully automatic process to produce highly
accurate digital image data. Three superimposed image tracks are acquired
simultaneously by three line sensors mounted in parallel on the instrument
focal plane. The imager gives as output the values of trichromatic coordinates
L*, a*, b* [24]. The imager was used in conjunction with a constant light
source (Reporter 21D MicroSun, 21 W, Sachtler,
Germany). The
chosen light source was equipped with a 21 W daylight discharge bulb (Sachtler, Germany
;
color temperature 5500–6000 K) which produces more light than normal
50 W tungsten luminaries. It has a luminous flux of 71 lumens per watt,
whereas in contrast, the best tungsten bulbs produce only 30lumens per
watt. No dichroic tungsten filter was used for the measurements. To ensure a
constant light intensity over all measurements and sampling dates the light
intensity was calibrated against known references as indicated by Graeff [25].Three plants per plot were used as a subsample of the
whole plot for the measurements. Images were taken without removing the leaf
from the plant. A leaf area of 4.5 cm2 was scanned at a
point about one-quarter of the way from the base to the tip. The leaf to be
measured was laid on a black aluminum plate mounted 15–20 cm away from
the optics (1.28/60 mm, LEICA, Germany) of the imager. The imager
was oriented vertically and scanned the reflectance through the visible (380–780 nm) and
NIR (750–1300 nm)
wavelength ranges at given long-pass filter intervals at a rate of 1 scan per
24 milliseconds. Selected long-pass filters (Maier Photonics, Manchester,
VT, USA) were active at wavelengths longer than 380 nm (400–2000 nm transm.
avg. 90%), 490 nm (500–2000 nm transm.
avg. 90%), 510 nm (520–2000 nm transm.
avg. 90%), 516 nm (525–2000 nm transm.
avg. 90%), 540 nm (550–2000 nm transm.
avg. 90%), and 600 nm (610–2000 nm transm.
avg. 90%), respectively. The long-pass filters had the following general
specifications: thickness 3 mm, hard-oxide coating surface, quality 80/50 per
MIL-O-13830A, coating quality 60/40 per MIL-O-13830A, and temperature limits
from −50 to 100°C.For each plant, images
were taken performed with the above-mentioned long-pass filters in conjunction
with an LEICA Daylight Filter IRa E55 to cut all scans at 780 nm (wavelength
ranges indicated withX780 nm). A second set of scans was taken
without this daylight filter in order to scan in the near-infrared ranges,
indicated with X1300 nm.Digital image data were
studied to select feature wavelengths, that is, the wavelengths at which
contrasts in spectral responses between major object categories became
distinct. Images were analyzed with ADOBE Photoshop 5.0 (file type*.psd,
8-bit) in the L*a*b*-color system [24] by splitting the images into a* and b*
parameters in different wavelength ranges. In the L*a*b*-color system the
values L*, a*, and b* are plotted using a Cartesian coordinate system. Equal
distances in the space approximately represent equal color differences. Value
L* represents lightness; value a* represents the red/green axis; and value b*
represents the yellow/blue axis. CIELAB is a popular color space for use in
measuring reflective and transmissive objects. Color features were defined
using spectral responses at these feature wavelengths in the form of a* and b*
parameters. Observations of the spectra of the 11 filter classes revealed that,
at certain wavelengths, the contrasts between major object categories were
maximized. The reflectance calibrations defined by Graeff et al. [22] and
Graeff and Claupein [26] were used as starting point to identify possible nitrogen
limiting conditions.


### 2.3. Color Theory

In the last couple of years, low-cost digital imaging
methods have become readily available easing the set-up of calibrations for the
detection of different plant stresses. In 1986, the Commission Internationale
d'Eclairage (CIE) updated and refined the three-dimensional Lab color system,
which displays every color perceived by the human eye [24]. The values
represent a precise, objective, and unambiguous measure of color that is
independent of imaging capabilities [27]. The technique applied in this study uses
characteristics of the CIELAB chromaticity parameters and relates them
mathematically to the response of plants due to nitrogen demand. The evaluation
of the nitrogen demand is based on the mathematical expression of the
difference between CIELAB chromaticity values of a normal fed and a deficient
plant. Hence, the determined spectral features used to quantify plant N demand
should not be confused with human perceived colors. Giving an equation of
colorimetric alteration directly related to the nutritional status of a plan,
could help the final user in the interpretation of the raw data, regardless of
the color.


### 2.4. Analyses

After taking the images, the measured plants were
harvested and the fresh weight was determined at once. Plant samples were dried
at 60°C and total dry matter was determined. The scanned leaf was ground and
analyzed for total N concentration according to Dumas [28] by means of a
Heraeus macro-N-analyzer (Hanau,
Germany).N balance was calculated based on nitrogen uptake
calculated as the product of the crop biomass (dry weight) and the N
concentrations in plant material at final harvest date, theNmin left in the soil after harvest and the total input of nitrogen given as
fertilizer rate and Nmin in the soil at planting date.


### 2.5. Statistical Analysis

Analysis of variance (ANOVA) was carried out on all
crop and reflectance data using the general procedures of the Tukey minimum
significant difference (MSD) test at the 5% significance level of the Statistical
Analysis System (SAS) version 6.12. Regression analyses were performed to
determine the association among the plant nitrogen concentration and spectral
properties of the plants.


## 2.1. Field Experiments

Field studies were conducted at the experimental station
of horticulture (697 mm average annual precipitation; 8.8°C mean
annual temperature), of the University of Hohenheim, Stuttgart,
Germany.
The first field trial consisted of two common broccoli cultivars “Parthenon”
and “Marathon” and four N fertilization
treatments consisting of 0, 100, 200, and 400 kg N
ha-1. The total amount of nitrogen was split in two rates in the 400 kg treatment, broadcast and incorporated in all treatments by hand as KAS
(13.5% NH4-N, 13.5% NO3-N, 12% CaO). This
field trial was used to set up to derive a relationship between spectral
properties of broccoli plants and nitrogen demand and to establish the
necessary calibrations for the estimation of N fertilizer amounts.In a second field trial the same varieties were tested
under six different N fertilization treatments consisting of a final
amount of 0, 76, 152, 200, 248 and 304 kg N ha-1. The total
amount of nitrogen was split in two rates, whereof 50% was applied in the
first rate at setting, broadcast and incorporated in the treatments up to 304 kg ha-1 as KAS (13.5% NH4-N, 13.5% NO3-N,
12% CaO) and in the 248 kg ha-1 treatment as ENTEC26
applied at setting. The amount of the second nitrogen rate was calculated based
on the N demand of broccoli plants derived out of the digital image analysis
and thus to validate the derived calibrations.Broccoli was planted in both trials on July 05th in 06
at a rate of 4.5 plants per m2. The trial set
up was a randomized block design with four replications. Timing of N
application was set to start of cultivation, that is, at setting and after a
four-week growing period (before beginning of floret formation while period of
leaf formation). Soil Nmin content was determined in all plots at
planting and after final harvest to calculate an N balance for each treatment. Broccoli
plants were irrigated to ensure a nonlimiting water supply throughout the main
growing period. Soil nutrient status analyses were made at the beginning of
vegetation period to avoid any other nutrient deficiencies in broccoli other
than nitrogen. Plant protection measures were carried out as needed to control
weeds and pests. Harvest of broccoli florets started in the mid of September as
soon as 50% of the plants of a plot had reached a minimum marketable head
size of ∅7 cm or tended to show further flower
development. Two harvests were accomplished in a four-day interval. Florets
were trimmed to market specifications for broccoli and graded for diameter,
weight, discoloration, and hollow stem.


## 2.2. Image Acquisition

Image acquisition started two weeks after planting and
was carried out once a week on the youngest fully developed leaf of each plant
until final harvest of broccoli florets. Images were taken with a digital,
light-sensitive (ISO 200–2400; spectral
sensitivity of 250–1300 nm),
high-spatial resolution (5140*5140 pixel) imager (S1 PRO, Leica, Germany).
Settings of the imager were chosen according to Graeff [25]. The imager
provides an entirely digital and fully automatic process to produce highly
accurate digital image data. Three superimposed image tracks are acquired
simultaneously by three line sensors mounted in parallel on the instrument
focal plane. The imager gives as output the values of trichromatic coordinates
L*, a*, b* [24]. The imager was used in conjunction with a constant light
source (Reporter 21D MicroSun, 21 W, Sachtler,
Germany). The
chosen light source was equipped with a 21 W daylight discharge bulb (Sachtler, Germany
;
color temperature 5500–6000 K) which produces more light than normal
50 W tungsten luminaries. It has a luminous flux of 71 lumens per watt,
whereas in contrast, the best tungsten bulbs produce only 30lumens per
watt. No dichroic tungsten filter was used for the measurements. To ensure a
constant light intensity over all measurements and sampling dates the light
intensity was calibrated against known references as indicated by Graeff [25].Three plants per plot were used as a subsample of the
whole plot for the measurements. Images were taken without removing the leaf
from the plant. A leaf area of 4.5 cm2 was scanned at a
point about one-quarter of the way from the base to the tip. The leaf to be
measured was laid on a black aluminum plate mounted 15–20 cm away from
the optics (1.28/60 mm, LEICA, Germany) of the imager. The imager
was oriented vertically and scanned the reflectance through the visible (380–780 nm) and
NIR (750–1300 nm)
wavelength ranges at given long-pass filter intervals at a rate of 1 scan per
24 milliseconds. Selected long-pass filters (Maier Photonics, Manchester,
VT, USA) were active at wavelengths longer than 380 nm (400–2000 nm transm.
avg. 90%), 490 nm (500–2000 nm transm.
avg. 90%), 510 nm (520–2000 nm transm.
avg. 90%), 516 nm (525–2000 nm transm.
avg. 90%), 540 nm (550–2000 nm transm.
avg. 90%), and 600 nm (610–2000 nm transm.
avg. 90%), respectively. The long-pass filters had the following general
specifications: thickness 3 mm, hard-oxide coating surface, quality 80/50 per
MIL-O-13830A, coating quality 60/40 per MIL-O-13830A, and temperature limits
from −50 to 100°C.For each plant, images
were taken performed with the above-mentioned long-pass filters in conjunction
with an LEICA Daylight Filter IRa E55 to cut all scans at 780 nm (wavelength
ranges indicated withX780 nm). A second set of scans was taken
without this daylight filter in order to scan in the near-infrared ranges,
indicated with X1300 nm.Digital image data were
studied to select feature wavelengths, that is, the wavelengths at which
contrasts in spectral responses between major object categories became
distinct. Images were analyzed with ADOBE Photoshop 5.0 (file type*.psd,
8-bit) in the L*a*b*-color system [24] by splitting the images into a* and b*
parameters in different wavelength ranges. In the L*a*b*-color system the
values L*, a*, and b* are plotted using a Cartesian coordinate system. Equal
distances in the space approximately represent equal color differences. Value
L* represents lightness; value a* represents the red/green axis; and value b*
represents the yellow/blue axis. CIELAB is a popular color space for use in
measuring reflective and transmissive objects. Color features were defined
using spectral responses at these feature wavelengths in the form of a* and b*
parameters. Observations of the spectra of the 11 filter classes revealed that,
at certain wavelengths, the contrasts between major object categories were
maximized. The reflectance calibrations defined by Graeff et al. [22] and
Graeff and Claupein [26] were used as starting point to identify possible nitrogen
limiting conditions.


## 2.3. Color Theory

In the last couple of years, low-cost digital imaging
methods have become readily available easing the set-up of calibrations for the
detection of different plant stresses. In 1986, the Commission Internationale
d'Eclairage (CIE) updated and refined the three-dimensional Lab color system,
which displays every color perceived by the human eye [24]. The values
represent a precise, objective, and unambiguous measure of color that is
independent of imaging capabilities [27]. The technique applied in this study uses
characteristics of the CIELAB chromaticity parameters and relates them
mathematically to the response of plants due to nitrogen demand. The evaluation
of the nitrogen demand is based on the mathematical expression of the
difference between CIELAB chromaticity values of a normal fed and a deficient
plant. Hence, the determined spectral features used to quantify plant N demand
should not be confused with human perceived colors. Giving an equation of
colorimetric alteration directly related to the nutritional status of a plan,
could help the final user in the interpretation of the raw data, regardless of
the color.


## 2.4. Analyses

After taking the images, the measured plants were
harvested and the fresh weight was determined at once. Plant samples were dried
at 60°C and total dry matter was determined. The scanned leaf was ground and
analyzed for total N concentration according to Dumas [28] by means of a
Heraeus macro-N-analyzer (Hanau,
Germany).N balance was calculated based on nitrogen uptake
calculated as the product of the crop biomass (dry weight) and the N
concentrations in plant material at final harvest date, theNmin left in the soil after harvest and the total input of nitrogen given as
fertilizer rate and Nmin in the soil at planting date.


## 2.5. Statistical Analysis

Analysis of variance (ANOVA) was carried out on all
crop and reflectance data using the general procedures of the Tukey minimum
significant difference (MSD) test at the 5% significance level of the Statistical
Analysis System (SAS) version 6.12. Regression analyses were performed to
determine the association among the plant nitrogen concentration and spectral
properties of the plants.


## 3. Results and Discussion

### 3.1. Yield

Climatic conditions in 2006 were favorable
for growth of broccoli with additional irrigation. Yield of broccoli cv.
Parthenon exceeded yield of cv. Marathon in
both trials. Figure1 provides the results of yield of both cultivars and both
field experiments depending on the different N fertilizer treatments. Yield
levels increased with N application rates. Maximum yield of 215 dt ha-1 was obtained with the treatment of 200 kg N ha-1 of N, while no
further yield increases were obtained at higher N levels (400 kg ha-1).
In other studies, the maximum reported yields ranged from 100 to 150 dt ha-1 [29, 30]. These authors reported an optimum N level of 250 kg ha-1 to attain a yield of 95 dt ha-1. In our study, the optimum N rate
was lower, but resulted in higher yields, probably due to different growing
conditions. Floret diameter and floret weight of both varieties were most
responsive to N rates across the range of treatments. No quality parameter
maxima were predicted within the range of treatments, except for floret weight.Marketable yield of broccoli cv. Marathon and Parthenon [dt ha-1] for different N fertilizer levels in the two field trials (trial 1 = a, trial 2 = b). Significant differences are indicated for fertilizer treatments at the α=0.05 probability level.
(a)(b)Visual inspection of the different N
treatments in the plots provided an estimate of the fresh market quality
variability within each plot and within each fertilizer treatment. Visual
inspection confirmed variability reduction in market quality characteristics
such as light green to yellow appearance and floret and plant size in plots
treated with nitrogen rates >200 kg ha-1. The 200
and 400 kg N ha-1 plots were more consistent in their
dark green appearance as well as in plant and floret size throughout the plots
than the 0 and 100 kg N ha-1 plots. However, the
quality parameters discoloration and hollow stem were not different across the
treatments.Except at the
lowest N rate, both varieties showed excellent yield and quality across the
range of N treatments. Predicted maximum yields and quality occurred at N rates
>200 kg ha-1 (Figure 1) indicating that under the local field
conditions the optimum fertilizer amount for row crop broccoli production
consists of an adequate application of N > 200 kg ha-1 for both
cultivars. These N rates are lower than currently recommended rates of >300 kg ha-1 [30, 31]. However, Stivers et al. [32] reported that
broccoli growers may apply as much as 400 kg N ha-1 to achieve high
yields. Other researchers have noted a positive broccoli yield response to N
rates as high as 330 kg ha-1 in California [33] and >500 kg ha-1 in British
Columbia
[34]. The N rates associated with maximum
yield in our experiments are likely lower than would be needed in a normal
production situation, as soil mineralization was high due to clover-grass as
previous crop.


### 3.2. Image Analysis—Calibrations for
N Demand

Figures2(a) and 2(b) indicate that the b* parameter of both varieties increased
significantly in the wavelength range 516780 nm with decreasing N fertilization
rates and thus decreasing N rates in leaf dry matter. Reflectance spectra of young
unfolded leaves of the treatment 0 N kg ha-1 were significantly
different from the reflectance spectra of 200 N kg ha-1 and 400 kg
ha-1 leaves starting from T2 for the variety Marathon and T3 for the variety Parthenon. Significant changes in determined color features expressed by a* and b* parameters revealed
that certain wavelengths especially in the visible spectra maximized the
contrasts between a* and b* parameters and thus between N levels. Over all
tested wavelength ranges, the wavelength ranges 510780 nm, 516780 nm, and 380780 nm seemed to be well suited for the identification of broccoli N status.
Further, the results also indicated that due to genetic variation
among tested cultivars and thus different development and N-uptake rates, the
spectral signatures differed between the varieties expressed as absolute values
and the timing the N concentration decreased below a certain critical level. Especially
cv. Marathon seems to develop earlier and
faster than cv. Parthenon, necessitating an earlier N fertilizer application than the variety Parthenon to sustain leaf
development (Figures 2(a), 2(b)). Further, using a* and b* parameters
individually lead to better results than using a combination of both
parameters.Reflectance changes of broccoli cv. Parthenon (a) and Marathon (b) for different N fertilizer levels over a period of 6 weeks (T1–T6) indicated by theb* parameter in the wavelength range 516780 nm. Significant differences (α=0.05) are indicated between N rates at the different measurement dates (T1 = after planting−T6 = harvest).
(a)(b)The constancy of modified reflectance in the
visible region as a response to nitrogen level was evident among the different
N fertilizer treatments. It was obvious that a successful identification and
quantification of nitrogen status was possible. The wavelength band in the
green range (500–600 nm)
seemed to be the most important wavelength band for the determination of nitrogen
status. Reflectance in the visible region of the spectra is mainly influenced
by pigmental changes. Gates et al. [35] found that the characteristics of plant
reflectance are functions of leaf geometry, morphology, physiology, and
biochemistry. They are influenced by soil and climatic conditions [35] and
nutrient status [36]. Excess or deficiency of an essential element may cause
visible abnormalities in pigmentation, size, and shape of leaves [19]. We
suppose that N status affected leaf pigments (mainly chlorophyll) of the
measured leaves during the growing process and led to the determined
reflectance changes.To relate leaf
reflectance response to N demand, the parameterb* was correlated with the
chemically determined N concentration in leaf dry matter. The relationship was
best described by a linear model. Equations (1) represent the mathematical
relationship used to quantify plant N through CIELAB chromaticity parameters
for each tested cultivar:(1)F(xParthenon)=164.31−2.05x,F(xMarathon)=167.48−2.79x.Plants were
analyzed at several growth stages to develop sufficiency levels for N. Leaf N
concentration varied depending on the fertilizer treatment and growth stage between
2% N and 8% N. Total N ranged from 5.5 to 8% from the early
growth stages until first bud, but was reduced to 4–6% at floret
formation and to 2–5% during
the preharvest stage. Lower sufficiency values at heading have been reported by
others [37, 38]. Nitrogen uptake by broccoli typically ranges from 150 to 280 kg ha-1 [30, 32]. Rates of N used by broccoli growers may be as high
as 400 kg ha-1 [32] whereof broccoli florets typically account for
10 to 45% of the N taken-up. Broccoli is a rapidly-growing crop that
takes up little N in its first 40 d of growth, and 90% or more of its
total N accumulation may occur during the final 30 to 50 d preceding harvest [30].
Broccoli plants are highly responsive to N fertilizer inputs, and excessive N
inputs can result in decreased quality like hollow stem [32]. According to
Bergmann [39] N≥3.0% was defined as adequate nutrient
concentration in the plant dry matter around heading. In consequence, the
amount of N applied in the 200 kg ha-1 treatment was considered as
nonlimiting during the whole time of the experiment, because leaf N was higher
than 3.0%. Therefore, reflectance values of the 200 kg N ha-1 treatment were taken as a reference for an adequately N fed broccoli plant in a
high input production system and as a basis to calculate necessary N fertilizer
amounts around floret formation.Over all treatments and both cultivars,
the parameterb* increased in the visible spectrum, when N was deficient. The
relation of reflectance parameter b* and leaf N concentration was best
described by a linear model in the visible wavelength ranges (Figures 3(a), 3(b)).
Some of the calibration curves using particular wavelength ranges especially in
the infrared predicted leaf N concentration poorly (R = 0.1) (results not
shown). The highest correlation coefficients were obtained in the wavelength
ranges 510780 nm (R = 0.85 for “Marathon”;
R = 0.83 for “Parthenon”) (Figures 3(a), 3(b)), 516780 nm (R = 0.72), and
380780 nm (R = 0.71) (results not shown). The derived equations
f (Parthenon 516780 nm) = 164.310−2.059*x and f (Marathon 516780 nm) = 167.486 − (2.793*x) were used to calculate necessary
N fertilizer amounts for the second N application rate in the validation field
trial.Linear regression between the N concentration in leaf dry matter [%] and the parameterb* in the wavelength range 516780 nm for the broccoli cultivars Parthenon (a) and Marathon (b). A correlation coefficient of R=0.81 was obtained for Parthenon and R=0.82 was obtained for Marathon over the tested nitrogen levels.
(a)(b)Knowledge of crop
nitrogen demand is essential in predicting crop needs and, therefore, in
developing reliable fertilizer recommendations for growers. Such
recommendations are critical, for both economic and environmental reasons [40].
The N demand of a crop is defined as the N uptake over a set period, which
allows the maximum dry matter growth rate under a given set of environmental
conditions [41]. The conventional fertilization method of vegetables in the field
provides high N amounts to ensure a constant N supply to the plant throughout
the crop cycle. The conventional optimal N rate is selected as the one allowing
just maximal yield at harvest. Since N demand is variable over time, each plant
growth stage and also each variety may have a specific N rate at which its
growth rate is the fastest. Consequently, the conventional optimal N rates may
either underestimate the instantaneous N demand (N uptake rate allowing just
maximum growth), at the stages where it is the highest, or overestimate it, at
the stages where it is the lowest. This will lead either to yield and quality
reduction, or to high N amounts left in the soil after harvest. Especially in
this context, the fast and easy determination of broccoli N demand in order to
properly estimate needed N fertilizer amounts by the use of digital image
analysis gains in importance.


### 3.3. Validation of Calculated N Amounts

Decisions needed to give appropriate advice on the
amounts and timing of N to be applied to crops depend on a complex interaction
of processes which can be represented in mathematical models. Determined
regressions between spectral parameters and chemically determined nitrogen
amounts were used to calculate the necessary N fertilizer amounts for the
second N application rate in field trial 2 around floret formation. Digital
images were taken around floret formation and evaluated for changes in theb*
parameter. Determined b* parameters replaced the variables in the equations of
the calibration process and let to final calculated N fertilizer amounts.
Determined N amounts were applied directly in the field.The comparison of determined N rates by digital image
analysis with the standard application rate of farmers indicated that
determined N rates based on digital image analysis were 100 kg ha-1 lower than the standard application rate, while no significant changes were
observed between yield and quality levels of these treatments (Figure 1). Further,
the N balance showed a high and efficient N uptake of broccoli plants of up to
365 kg ha-1, indicating a high N utilization rate without leaving
high N amounts in the soil after harvest (Figure 4). Other researchers have
also reported broccoli aboveground N uptake well in excess of 300 kg ha-1 [34, 42]. However, the pattern of N uptake, with low N uptake early in the season
followed by a period of rapidly increasing uptake, illustrates the management
challenge posed by broccoli production. An adequate supply of N is required all
season. Preplant or early-season applications of N may be inefficiently used
because of low plant N demand. N application around floret formation was fully
used by the broccoli plants in our experiment, wherefore a split of nitrogen
fertilizer amounts is highly recommended in broccoli production. Estimating the
N demand of broccoli plants around floret formation by fast and easy spectral
measurements will help to improve yield and quality and contribute to the
reduction of N left in the soil after harvest in intensive horticultural
cropping systems. Overall, the results suggest that digital image analysis and
reflectance data can be a nondestructive, rapid, cost-effective, and
reproducible technique to determine N demand in broccoli with repeatable
accuracy and precision.Figure 4
N-balance [kg N ha-1] of the validation trial for the different fertilizer treatments (mean over both varieties).Overall, this paper
presents a new and simple technique to derive quantitative estimates of broccoli
nitrogen status using CIELAB chromaticity parameters as information source from
digital images. This technique is based on the association of changing leaf
color due to nitrogen status of broccoli plants. Nitrogen status is reflected
by with a representative wavelength region on a two-dimensional colour space,
and applies to cases of single leaf measurements under controlled light
conditions. The efficacy of the method is demonstrated using sets of samples
obtained from both field and laboratory studies. It is shown that application
of the proposed approach results in a highly linear relationship between chromaticity
parameters and nitrogen status of broccoli plants. The method outlined here
allows for a substantial improvement in the speed of sample evaluation to
estimate broccoli N status both in the field and in the laboratory. The
technique of theL*, a*, and b* values defined for different N concentrations
in leaf dry matter provided a range of chromaticity parameters that can be
successfully utilized in digital image screening processing.


## 3.1. Yield

Climatic conditions in 2006 were favorable
for growth of broccoli with additional irrigation. Yield of broccoli cv.
Parthenon exceeded yield of cv. Marathon in
both trials. Figure1 provides the results of yield of both cultivars and both
field experiments depending on the different N fertilizer treatments. Yield
levels increased with N application rates. Maximum yield of 215 dt ha-1 was obtained with the treatment of 200 kg N ha-1 of N, while no
further yield increases were obtained at higher N levels (400 kg ha-1).
In other studies, the maximum reported yields ranged from 100 to 150 dt ha-1 [29, 30]. These authors reported an optimum N level of 250 kg ha-1 to attain a yield of 95 dt ha-1. In our study, the optimum N rate
was lower, but resulted in higher yields, probably due to different growing
conditions. Floret diameter and floret weight of both varieties were most
responsive to N rates across the range of treatments. No quality parameter
maxima were predicted within the range of treatments, except for floret weight.Marketable yield of broccoli cv. Marathon and Parthenon [dt ha-1] for different N fertilizer levels in the two field trials (trial 1 = a, trial 2 = b). Significant differences are indicated for fertilizer treatments at the α=0.05 probability level.
(a)(b)Visual inspection of the different N
treatments in the plots provided an estimate of the fresh market quality
variability within each plot and within each fertilizer treatment. Visual
inspection confirmed variability reduction in market quality characteristics
such as light green to yellow appearance and floret and plant size in plots
treated with nitrogen rates >200 kg ha-1. The 200
and 400 kg N ha-1 plots were more consistent in their
dark green appearance as well as in plant and floret size throughout the plots
than the 0 and 100 kg N ha-1 plots. However, the
quality parameters discoloration and hollow stem were not different across the
treatments.Except at the
lowest N rate, both varieties showed excellent yield and quality across the
range of N treatments. Predicted maximum yields and quality occurred at N rates
>200 kg ha-1 (Figure 1) indicating that under the local field
conditions the optimum fertilizer amount for row crop broccoli production
consists of an adequate application of N > 200 kg ha-1 for both
cultivars. These N rates are lower than currently recommended rates of >300 kg ha-1 [30, 31]. However, Stivers et al. [32] reported that
broccoli growers may apply as much as 400 kg N ha-1 to achieve high
yields. Other researchers have noted a positive broccoli yield response to N
rates as high as 330 kg ha-1 in California [33] and >500 kg ha-1 in British
Columbia
[34]. The N rates associated with maximum
yield in our experiments are likely lower than would be needed in a normal
production situation, as soil mineralization was high due to clover-grass as
previous crop.


## 3.2. Image Analysis—Calibrations for
N Demand

Figures2(a) and 2(b) indicate that the b* parameter of both varieties increased
significantly in the wavelength range 516780 nm with decreasing N fertilization
rates and thus decreasing N rates in leaf dry matter. Reflectance spectra of young
unfolded leaves of the treatment 0 N kg ha-1 were significantly
different from the reflectance spectra of 200 N kg ha-1 and 400 kg
ha-1 leaves starting from T2 for the variety Marathon and T3 for the variety Parthenon. Significant changes in determined color features expressed by a* and b* parameters revealed
that certain wavelengths especially in the visible spectra maximized the
contrasts between a* and b* parameters and thus between N levels. Over all
tested wavelength ranges, the wavelength ranges 510780 nm, 516780 nm, and 380780 nm seemed to be well suited for the identification of broccoli N status.
Further, the results also indicated that due to genetic variation
among tested cultivars and thus different development and N-uptake rates, the
spectral signatures differed between the varieties expressed as absolute values
and the timing the N concentration decreased below a certain critical level. Especially
cv. Marathon seems to develop earlier and
faster than cv. Parthenon, necessitating an earlier N fertilizer application than the variety Parthenon to sustain leaf
development (Figures 2(a), 2(b)). Further, using a* and b* parameters
individually lead to better results than using a combination of both
parameters.Reflectance changes of broccoli cv. Parthenon (a) and Marathon (b) for different N fertilizer levels over a period of 6 weeks (T1–T6) indicated by theb* parameter in the wavelength range 516780 nm. Significant differences (α=0.05) are indicated between N rates at the different measurement dates (T1 = after planting−T6 = harvest).
(a)(b)The constancy of modified reflectance in the
visible region as a response to nitrogen level was evident among the different
N fertilizer treatments. It was obvious that a successful identification and
quantification of nitrogen status was possible. The wavelength band in the
green range (500–600 nm)
seemed to be the most important wavelength band for the determination of nitrogen
status. Reflectance in the visible region of the spectra is mainly influenced
by pigmental changes. Gates et al. [35] found that the characteristics of plant
reflectance are functions of leaf geometry, morphology, physiology, and
biochemistry. They are influenced by soil and climatic conditions [35] and
nutrient status [36]. Excess or deficiency of an essential element may cause
visible abnormalities in pigmentation, size, and shape of leaves [19]. We
suppose that N status affected leaf pigments (mainly chlorophyll) of the
measured leaves during the growing process and led to the determined
reflectance changes.To relate leaf
reflectance response to N demand, the parameterb* was correlated with the
chemically determined N concentration in leaf dry matter. The relationship was
best described by a linear model. Equations (1) represent the mathematical
relationship used to quantify plant N through CIELAB chromaticity parameters
for each tested cultivar:(1)F(xParthenon)=164.31−2.05x,F(xMarathon)=167.48−2.79x.Plants were
analyzed at several growth stages to develop sufficiency levels for N. Leaf N
concentration varied depending on the fertilizer treatment and growth stage between
2% N and 8% N. Total N ranged from 5.5 to 8% from the early
growth stages until first bud, but was reduced to 4–6% at floret
formation and to 2–5% during
the preharvest stage. Lower sufficiency values at heading have been reported by
others [37, 38]. Nitrogen uptake by broccoli typically ranges from 150 to 280 kg ha-1 [30, 32]. Rates of N used by broccoli growers may be as high
as 400 kg ha-1 [32] whereof broccoli florets typically account for
10 to 45% of the N taken-up. Broccoli is a rapidly-growing crop that
takes up little N in its first 40 d of growth, and 90% or more of its
total N accumulation may occur during the final 30 to 50 d preceding harvest [30].
Broccoli plants are highly responsive to N fertilizer inputs, and excessive N
inputs can result in decreased quality like hollow stem [32]. According to
Bergmann [39] N≥3.0% was defined as adequate nutrient
concentration in the plant dry matter around heading. In consequence, the
amount of N applied in the 200 kg ha-1 treatment was considered as
nonlimiting during the whole time of the experiment, because leaf N was higher
than 3.0%. Therefore, reflectance values of the 200 kg N ha-1 treatment were taken as a reference for an adequately N fed broccoli plant in a
high input production system and as a basis to calculate necessary N fertilizer
amounts around floret formation.Over all treatments and both cultivars,
the parameterb* increased in the visible spectrum, when N was deficient. The
relation of reflectance parameter b* and leaf N concentration was best
described by a linear model in the visible wavelength ranges (Figures 3(a), 3(b)).
Some of the calibration curves using particular wavelength ranges especially in
the infrared predicted leaf N concentration poorly (R = 0.1) (results not
shown). The highest correlation coefficients were obtained in the wavelength
ranges 510780 nm (R = 0.85 for “Marathon”;
R = 0.83 for “Parthenon”) (Figures 3(a), 3(b)), 516780 nm (R = 0.72), and
380780 nm (R = 0.71) (results not shown). The derived equations
f (Parthenon 516780 nm) = 164.310−2.059*x and f (Marathon 516780 nm) = 167.486 − (2.793*x) were used to calculate necessary
N fertilizer amounts for the second N application rate in the validation field
trial.Linear regression between the N concentration in leaf dry matter [%] and the parameterb* in the wavelength range 516780 nm for the broccoli cultivars Parthenon (a) and Marathon (b). A correlation coefficient of R=0.81 was obtained for Parthenon and R=0.82 was obtained for Marathon over the tested nitrogen levels.
(a)(b)Knowledge of crop
nitrogen demand is essential in predicting crop needs and, therefore, in
developing reliable fertilizer recommendations for growers. Such
recommendations are critical, for both economic and environmental reasons [40].
The N demand of a crop is defined as the N uptake over a set period, which
allows the maximum dry matter growth rate under a given set of environmental
conditions [41]. The conventional fertilization method of vegetables in the field
provides high N amounts to ensure a constant N supply to the plant throughout
the crop cycle. The conventional optimal N rate is selected as the one allowing
just maximal yield at harvest. Since N demand is variable over time, each plant
growth stage and also each variety may have a specific N rate at which its
growth rate is the fastest. Consequently, the conventional optimal N rates may
either underestimate the instantaneous N demand (N uptake rate allowing just
maximum growth), at the stages where it is the highest, or overestimate it, at
the stages where it is the lowest. This will lead either to yield and quality
reduction, or to high N amounts left in the soil after harvest. Especially in
this context, the fast and easy determination of broccoli N demand in order to
properly estimate needed N fertilizer amounts by the use of digital image
analysis gains in importance.


## 3.3. Validation of Calculated N Amounts

Decisions needed to give appropriate advice on the
amounts and timing of N to be applied to crops depend on a complex interaction
of processes which can be represented in mathematical models. Determined
regressions between spectral parameters and chemically determined nitrogen
amounts were used to calculate the necessary N fertilizer amounts for the
second N application rate in field trial 2 around floret formation. Digital
images were taken around floret formation and evaluated for changes in theb*
parameter. Determined b* parameters replaced the variables in the equations of
the calibration process and let to final calculated N fertilizer amounts.
Determined N amounts were applied directly in the field.The comparison of determined N rates by digital image
analysis with the standard application rate of farmers indicated that
determined N rates based on digital image analysis were 100 kg ha-1 lower than the standard application rate, while no significant changes were
observed between yield and quality levels of these treatments (Figure 1). Further,
the N balance showed a high and efficient N uptake of broccoli plants of up to
365 kg ha-1, indicating a high N utilization rate without leaving
high N amounts in the soil after harvest (Figure 4). Other researchers have
also reported broccoli aboveground N uptake well in excess of 300 kg ha-1 [34, 42]. However, the pattern of N uptake, with low N uptake early in the season
followed by a period of rapidly increasing uptake, illustrates the management
challenge posed by broccoli production. An adequate supply of N is required all
season. Preplant or early-season applications of N may be inefficiently used
because of low plant N demand. N application around floret formation was fully
used by the broccoli plants in our experiment, wherefore a split of nitrogen
fertilizer amounts is highly recommended in broccoli production. Estimating the
N demand of broccoli plants around floret formation by fast and easy spectral
measurements will help to improve yield and quality and contribute to the
reduction of N left in the soil after harvest in intensive horticultural
cropping systems. Overall, the results suggest that digital image analysis and
reflectance data can be a nondestructive, rapid, cost-effective, and
reproducible technique to determine N demand in broccoli with repeatable
accuracy and precision.Figure 4
N-balance [kg N ha-1] of the validation trial for the different fertilizer treatments (mean over both varieties).Overall, this paper
presents a new and simple technique to derive quantitative estimates of broccoli
nitrogen status using CIELAB chromaticity parameters as information source from
digital images. This technique is based on the association of changing leaf
color due to nitrogen status of broccoli plants. Nitrogen status is reflected
by with a representative wavelength region on a two-dimensional colour space,
and applies to cases of single leaf measurements under controlled light
conditions. The efficacy of the method is demonstrated using sets of samples
obtained from both field and laboratory studies. It is shown that application
of the proposed approach results in a highly linear relationship between chromaticity
parameters and nitrogen status of broccoli plants. The method outlined here
allows for a substantial improvement in the speed of sample evaluation to
estimate broccoli N status both in the field and in the laboratory. The
technique of theL*, a*, and b* values defined for different N concentrations
in leaf dry matter provided a range of chromaticity parameters that can be
successfully utilized in digital image screening processing.


## 4. Conclusion

The results of this study represent
a first step of using CIELAB chromaticity parameters to evaluate the nitrogen
status of broccoli plants and to derive N fertilizer recommendations out of
determined spectral values. The approach using leaf reflectance parameters
derived from high-resolution digital images indicated to be an effective
technique for estimating N fertilizer amounts for broccoli around floret
formation in specific wavelength ranges. The spectral properties showed high
correlations with chemically determined N concentrations and required less time
and labor than current standard methods. Reflectance measurements appear to be
sufficiently discriminating between different nitrogen fertilizations to permit
practical use and to help growers make better-informed management decisions
about nitrogen and fertilizer applications during the season. However, before
such tools can be incorporated into the nitrogen management strategy of
horticultural crops, threshold values first have to be established as a
criterion for crop nitrogen supplementation. Furthermore, differences in
seasonal development of different cultivars might necessitate multiple time
frames for an in-season fertilization treatment. Evaluation will continue.



---

*Source: 359760-2008-07-28.xml*