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Crop Protection 27 (2008) 25–35 www.elsevier.com/locate/cropro
Estimating cabbage physical parameters using remote sensing technology Chenghai Yanga,, Tong-Xian Liub, James H. Everitta a
USDA-ARS, Kika de la Garza Subtropical Agricultural Research Center, 2413 E. Highway 83, Weslaco, TX 78596, USA Texas A&M University System, Texas Agricultural Experiment Station, 2415 E. Highway 83, Weslaco, TX 78596, USA
b
Received 18 September 2006; received in revised form 14 March 2007; accepted 18 April 2007
Abstract Remote sensing has long been used as a tool to extract plant growth and yield information for many crops, but little research has been conducted on cabbage (Brassica oleracea var. capitata L.) with this technology. The objective of this study was to evaluate aerial photography and field reflectance spectra for estimating cabbage physical parameters. An experiment was conducted on a cabbage field with 81 experimental plots to which different insecticide treatments were applied. Aerial color-infrared (CIR) photographs were taken from the field shortly before harvest. Meanwhile, field reflectance spectra and four plant physical parameters, including plant diameter, head diameter, plant weight and head weight, were measured from a total of 243 plants (three plants per plot). Plant area and spectral digital values for the near-infrared, red and green bands for each of the 243 plants were extracted from a digitized aerial CIR photograph. Four different vegetation indices, including the normalized difference vegetation index (NDVI), were calculated. Correlation analysis showed that the cabbage physical parameters were significantly related to the photo-derived plant area and spectral variables. Regression analysis showed that head weight was linearly related to plant area with an r2 value of 0.91 and quadratically related to NDVI with an r2 value of 0.66. Stepwise regression performed on cabbage head weight and 601 bands from 400 to 1000 nm in the field reflectance spectra revealed that 71% of the variability in head weight could be explained by eight significant bands in the spectra. As an application example, cabbage yield estimated from photo-derived plant area in each plot was used to compare the differences among 16 treatments. These results indicate that remote sensing can be a useful tool for evaluating cabbage growth and yield variations. r 2007 Elsevier Ltd. All rights reserved.
Keywords: Aerial photography; Cabbage; Plant area; Reflectance spectra; Remote sensing; Vegetation indices; Yield
1. Introduction Remote sensing technology can provide quantitative and timely information on agricultural crops during the growing season and has been used to monitor plant growth conditions and obtain crop yield information for decades. A variety of remote sensing systems are available for data collection, including ground-based spectroradiometers, aerial photographic cameras, and satellite- and aircraft-based electronic imaging systems. Spectral observations in the visible and near-infrared (NIR) regions of the electromagCorresponding author. Tel.: +1 956 969 4824; fax: +1 956 969 4893.
E-mail address:
[email protected] (C. Yang). 0261-2194/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.cropro.2007.04.015
netic spectrum and vegetation indices calculated from these observations are indicators of plant canopy cover and the amount of photosynthetically active tissue in the canopy (Tucker, 1979; Wiegand and Richardson, 1984). Research in remote sensing has reinforced the conclusion that plants integrate the growing conditions they have experienced and express their response through the canopies produced (Wiegand and Richardson, 1990). Crop growth variables such as leaf area index, plant height, biomass and yield are found to be significantly related to spectral bands and vegetation indices derived from remote-sensing data for a wide range of crops (Tucker et al., 1980; Shibayama and Akiyama, 1991; Wiegand et al., 1994; Yang and Anderson, 1999; Thenkabail et al., 2000; Yang et al., 2000, 2004).
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Cabbage (Brassica oleracea var. capitata L.) is a member of the cole crop group, which also includes cauliflower, broccoli, collards, kale and Brussels sprouts. Cabbage is one of the world’s leading vegetables in terms of total production and is grown in numerous regions across the United States (Sances, 2000). In 2004, a total of 30,700 ha of cabbage was harvested for fresh market in the US with a total value of $347 million (NASS, 2005). California had the highest production in 2004, followed by New York, Georgia and Texas. Many other states also contributed smaller shares to the overall cabbage production. There are many different types of cabbage grown throughout the world, including green, red, and savoy varieties. Head shape varies from the standard round to flattened or pointed. Cabbage can be infested by insects such as aphids, leafminers, thrips, whiteflies, cabbage maggots, beetles, true bugs and caterpillars (Carr, 1979). Cabbage looper, Trichoplusia ni (Hu¨bner), and diamondback moth, Plutella xylostella (L.), have been the two most important pests on cole crops in south Texas and can be the important production limitation for these crops (Cartwright et al., 1987; Liu, 1999; Liu and Sparks, 1999). Stresses due to crop pests and diseases result in reduction in leaf area and light interception or loss of chlorophyll (chlorosis) and thus affect the photosynthetic efficiency of the plants. As a result, the reflectivity and emissivity of the crop canopy are affected. To evaluate the effectiveness of different cultural practices and chemical control methods for cabbage production, ground surveys and hand sampling are commonly used. Remote sensing techniques provide an alternative approach to the assessment and quantification of plant growth conditions. Although remote sensing has long been used as a tool to extract plant growth and yield information for many crops, information on the use of remote sensing for assessing cabbage growth conditions is lacking. Despite current use of more sophisticated remote sensing systems, aerial photography remains one of the most reliable and widely used forms of remotely sensed imagery because of its higher spatial resolution and relatively low cost. Anuta and MacDonald (1971) used digitized black and white as well as color-infrared (CIR) aerial photographs taken in 1969 during the Apollo 9 mission for identifying crop types. Wiegand et al. (1994) related cotton yield to individual bands and the normalized difference vegetation index (NDVI) derived from digitized CIR aerial photography for assessing soil salinity. Tomer et al. (1997) used the NIR, red and green bands from digitized aerial CIR photographs for assessing corn yield and nitrogen uptake variability. Plant et al. (2000) used NDVI calculated from digitized CIR aerial photographs for detecting water and nitrogen stress and for estimating cotton yield. The objective of this study was to evaluate digitized CIR aerial photography and field reflectance spectra for estimating four cabbage physical parameters, including
plant diameter, head diameter, plant weight and head weight. 2. Materials and methods 2.1. Study site A field experiment was conducted at the Texas A&M University System’s Agricultural Research and Extension Center at Weslaco, TX, in 2004. The field was divided into 81 experimental plots. Each plot consisted of two approximately 8-m long rows with a row spacing of 1 m. All plots were separated by two rows of grain sorghum as wind breaks along the row direction and by approximately 3-m wide alleys across the row direction. Cabbage (Copenhagen Market variety) was planted on 12 January and then thinned to approximately 40-cm spacing. Copenhagen Market cabbage is a ball-head-type heirloom cabbage. This old fashioned cabbage has established itself as a favorite among gardeners and cabbage fans worldwide. The cabbage heads are of a blue–green color and of an attractive, fine quality with a solid construction. The plots were fertilized, irrigated and cultivated according to standard cultural practices established at the Texas Agricultural Experiment Station at Weslaco. A randomized complete block design with 16 treatments (15 insecticide treatments and one control) and four blocks were arranged in 64 of the 81 plots. The other 17 plots received either no insecticide treatment or some of the treatments. Table 1 shows the insecticides and application rates used for each treatment. The materials were applied using a tractor-mounted sprayer with three ceramic hollow cone nozzles per row (TX-10, one over the plants, two on drops) at a pressure of 689.5 kPa (7.03 kg/cm2 or 100 psi). Each insecticide was mixed with water and applied at a rate of 280 L/ha (30 gal/acre) and a ground speed of 3.2 km/h (2 mph). Treatments T1–T3 and T7–T15 each had only one unique insecticide and each insecticide was applied four times on 10 March, and 1, 14 and 22 April. The other four treatments T3–T6 each involved two insecticides. The first insecticide in each of these treatments was applied on 10 March and 14 April, and the second alternated insecticide was applied on 1 and 22 April. This study was mainly concerned with remote sensing measurements of the variability in cabbage plant physical parameters caused by the insecticide treatments and other factors such as soil variability. Therefore, all 81 plots were used for the study. 2.2. Collection of aerial photographs, field reflectance spectra, and plant physical parameters Aerial photographs were taken of the cabbage field using a Fairchild type K-37 large format (23 cm 23 cm) mapping camera on 3 May 2004. The film used was Kodak Aerochrome CIR type 1443 film sensitive in
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Table 1 Insecticides and application rates for different insecticide treatments used in a cabbage field experiment in south Texas Treatment ID
Treatment
T1 T2 T3 T4
Novaluron Novaluron Novaluron Novaluron
T5 T6
Novaluron 0.83EC alt. with l-cyhalothrin 1EC Spinosad 2SC alt. with Indoxacarb 30WDG
101.8 alt. 33.5 87.2 alt. 84.0
T7 T8 T9 T10 T11 T12 T13 T14 T15 T16
Indoxacarb 30WDG Spinosad 2SC Spinosad 80% l-Cyhalothrin 1EC QRD 6047 50% QRD 6047 75% ABG 6405 (Bt) ABG 6406 (Bt) ABG 6064 (Bt) Untreated control
84.0 70.0 70.0 33.5 4.677 L/ha 7.015 L/ha 1120 1120 1120
0.83EC 0.83EC 0.83EC alt. with Indoxacarb 30WDG 0.83EC alt. with Spinosad 2SC
Rate (g a.i./ha)
Manufacturer
87.2 101.8 87.2 alt. 84.0 87.2 alt. 70.0
Chemtura, Middlebury, CT Chemtura, Middlebury, CT Chemtura, Middlebury, CT and Du Pont, Wilmington, DE Chemtura, Middlebury, CT and Dow AgroScience, Indianapolis, IN Chemtura, Middlebury, CT and Syngenta, Greensboro, NC Dow AgroScience, Indianapolis, IN and Du Pont, Wilmington, DE Du Pont, Wilmington, DE Dow AgroScience, Indianapolis, IN Dow AgroScience, Indianapolis, IN Syngenta, Greensboro, NC AgraQuest, Davis, CA AgraQuest, Davis, CA Valent BioScience, North Chicago, IL Valent BioScience, North Chicago, IL Valent BioScience, North Chicago, IL
the visible green (500–600 nm), red (600–700 nm) and NIR (700–900 nm) region of the spectrum. The camera had an aperture setting of f9.6 at 1/500 s and a 305-mm lens equipped with a Wratten 15 orange (minus blue) filter. A Cessna 404 aircraft with a camera port in the floor was used to acquire aerial CIR photographs at an altitude of 460 m above the ground level between 13:00 and 14:00 h local time under calm and sunny conditions. No stabilizer or inertial measurement device (IMU) was used to dampen or measure platform variations, but care was taken to minimize the effects of aircraft motion by stabilizing the aircraft at the predetermined flight altitude, speed and direction during photo acquisition. Field reflectance spectra were collected from a total of 243 randomly selected cabbage plants (three plants per plot) using a FieldSpec HandHeld spectroradiometer (Analytical Spectral Devices Inc., Boulder, CO) on 4 May 2004. The spectroradiometer was sensitive in the visible to NIR portion of the spectrum (325–1075 nm) with a spectral interval of 1 nm. Each spectrum was an average of 10 sample spectra measured over each plant. The spectroradiometer had a field of view of 251 and was held at 1.25 m above ground during data collection, resulting in a circular target area of 55 cm in diameter. This target area was also used later for digital number extraction from the digitized aerial photo. Four plant physical parameters including plant diameter, head diameter, plant weight and head weight were measured for the 243 cabbage plants on 6 May 2004. Plant and head diameters were first measured with a meter stick from each selected plant, and the plant was then cut at ground level and weighed. Finally, the loose leaves on the plant were removed and the head weight was determined. An electronic scale was used for measuring plant weight and head weight.
2.3. Plant area and spectral data extraction from an aerial photograph A CIR photographic transparency of the study site was digitized at 1500 dpi resolution using an Epson Expression 10000XL scanner (Seiko Epson Corporation, Long Beach, CA). The digitized CIR photograph consisted of three spectral bands (NIR, red and green). Pixels in each band had a spectral digital count value ranging from 0 to 255. The digitized CIR image was rectified to the Universal Transverse Mercator (UTM), World Geodetic Survey 1984 (WGS-84), Zone 14, coordinate system based on 15 ground control points taken within and around the field with a submeter-accuracy GPS Pathfinder Pro XRS system (Trimble Navigation Limited, Sunnyvale, CA). Image rectification was performed using ERDAS IMAGINE (Leica Geosystems Geospatial Imaging, LLC, Norcross, GA). The resulting ground pixel size was 2.7 cm. A scanned photographic image contains relative intensity values of light reflected from the photograph after passing filters that allow transmission of red, green and blue wavelengths. Red, green, and blue image bands indicate photographic halftones within NIR, red, and green bands, respectively. The band images can vary with photo acquisition conditions, film processing methods, and scanner settings. Therefore, the spectral values extracted from scanned images are relative and can only be used to compare the differences among objects observed within the same image. Comparison of absolute spectral values between images will require calibration by placing at least two ground calibration panels of known reflectance values at each imaging site. Fortunately, digital count numbers from a digitized photo are linearly related to reflectance values and can provide quantitative relations that are as good as calibrated reflectance data for within-image estimation.
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Therefore, uncalibrated digital spectral data from digitized CIR photos have been directly related to plant growth and yield data by many researchers (Wiegand et al., 1994; Tomer et al., 1997; Plant et al., 2000). In this study, only one digitized photo was used and it was not necessary to calibrate the digital count values in each band to reflectance. Plant area and spectral digital values in the NIR, red and green bands for the 243 selected plants were extracted from the digitized photograph using Adobe Photoshop (Adobe Systems Inc., San Jose, CA). For plant area determination, a circle or an oval was drawn over each selected plant on the digitized CIR photo to fit the plant perimeter, and the number of pixels within the enclosed area was determined. To determine the total area of the plants within each plot, the 2 8 m image area for each plot was classified into cabbage plant area and non-plant area using Adobe Photoshop. Although ERDAS IMAGINE could have been used, Adobe Photoshop was easier for this purpose. To extract the spectral values for the NIR, red and green bands from all the plants, a circle with a fixed diameter of 55 cm (corresponding to the target area of the reflectance spectra) was centered over each plant and the spectral values for the three bands were determined. Since plant diameter varied from less than 15 to over 55 cm within the plots based on field observations, ideally a circle that could cover the largest plant should be used. Too small a circle would exclude the outside portion of the plant leaves, while too large a circle would include portions of the leaves from the neighboring plants. A diameter of 55 cm was a good compromise that covered approximately 90% of the plants used for spectral data extraction and allowed only minimal leaf interference from the neighboring plants. 2.4. Calculation of vegetation indices Various vegetation indices (VIs) have been developed that reduce multiband observations to a single numerical index (Wiegand et al., 1991). These VIs are usually formed from combinations of visible and NIR wavebands. Two of the earliest and most widely used VIs are the simple ratio (NR ¼ NIR/red) (Jordan, 1969) and the normalized difference vegetation index [NDVI ¼ (NIRred)/(NIR+red)] (Rouse et al., 1973). Two other similar VIs include the band ratio (NG ¼ NIR/green) (Yang et al., 2001) and the green NDVI [GNDVI ¼ (NIRgreen)/(NIR+green)] (Gitelson et al., 1996). Yang et al. (2001) and Yang and Everitt (2002) used band ratios (NR and NG) as well as normalized differences NDVI and GNDVI derived from airborne CIR imagery to generate yield maps for delineating within-field spatial variability. In this study, the two band ratios (NR and NG) and two normalized differences (NDVI and GNDVI), were calculated from the spectral values for the three bands to measure canopy abundance. 2.5. Statistical analysis Correlation analysis was performed to calculate correlation coefficients (r) among four cabbage plant physical
parameters (plant diameter, head diameter, plant weight and head weight), eight photo-derived variables (plant area, three spectral bands and four vegetation indices), and 601 spectral bands from 400 to 1000 nm in the field reflectance spectra. The lower portion (325–399 nm) and the upper portion (1001–1075 nm) of the spectra were excluded from analysis because of the relatively large noise in these portions of spectra. Linear and polynomial regression was used to determine best fitting equations relating cabbage head weight to plant area and to each of the vegetation indices. Stepwise regression was used to determine the best-fitting equations for relating cabbage head weight to the three photo-derived spectral bands and to the 601 bands in the reflectance spectra. The area for all cabbage plants in each plot was converted to cabbage head weight based on the regression equation relating head weight to plant area. Then the total head weight for each plot was converted to yield (kg/ha) based on the plot area. Analysis of variance (ANOVA) on the randomized complete block design was performed based on estimated yield. Multiple comparisons among the 16 treatments were made using Fisher’s protected least significant difference (LSD). All statistical analyses were performed using SAS software (SAS Institute Inc., Cary, NC). 3. Results and discussion 3.1. Reflectance spectra of cabbage plants Fig. 1 presents representative reflectance spectra for a healthy cabbage plant with a diameter of 45 cm, a stressed plant with a diameter of 25 cm, and bare soil in the visible to NIR region of the spectrum. The spectra were taken when the spectroradiometer was held 1 m above the ground level. In the visible portion of the spectrum, chlorophyll controls much of the spectral response of normal green plants. Chlorophyll molecules absorb blue and red light for use in photosynthesis and much less of the green light is absorbed and more is reflected. Therefore, the reflectance for normal green plants is higher in the green region than in the blue and red regions. In the NIR portion of the spectrum, spectral response of normal plants is controlled not by plant pigments, but by the structure of the spongy mesophyll tissue in plant leaves. Much of the radiation in the NIR portion is reflected by the spongy leave tissue. Toward the red end of the visible spectrum, as the absorption of red light by chlorophyll pigments begins to decline, reflectance rises sharply and gradually flattens out in the NIR portion. The reflectance for the healthy cabbage plant agreed with the spectral behavior of normal plants. The reflectance curve for the bare soil was close to a straight line and soil reflectance increased with wavelengths gradually in the visible to NIR region of the spectrum. The reflectance spectrum for the stressed plant fell somewhere between the reflectance curves for the healthy plant and bare soil. Although the general shape of the reflectance
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Blue
Green
NIR
Healthy plant
50
Reflectance (%)
Red
29
40
30
Bare soil Stressed plant
20
10
0 400
500
600
700
800
900
1000
Wavelength (nm)
Fig. 1. Representative reflectance spectra for a healthy cabbage plant with a diameter of 45 cm (top plant), a stressed cabbage plant with a diameter of 25 cm (bottom plant), and bare soil in the visible (400–700 nm) to NIR (700–1000 nm) region of the spectrum. The spectroradiometer was held at 1 m above ground and had a field of view of 44 cm in diameter (dark circles over the plants).
spectrum for the stressed plant was similar to that of the healthy plant, the stressed plant had higher reflectance in the visible region and lower reflectance in the NIR region than the healthy plant. These deviations in reflectance were partially due to the change in vegetative vigor for the stressed plant and partially attributed by relatively small canopy cover and large soil exposure within the field of view of the spectroradiometer. In fact, the healthy plant was slightly larger than the field of view of the instrument (44 cm in diameter). The stressed plant had a diameter of 25 cm and occupied only about 32% of the instrument’s field of view. Thus, the reflectance spectrum for the healthy plant was almost a pure spectrum for the cabbage plant, even though the soil background had some effect because the plant canopy was not perfectly a circle. On the other hand, the reflectance spectrum from the stressed plant was approximately a combination of 32% of the plant spectrum and 68% of soil background, including bare soil, dry leaves and other residual. Evidently, if a cabbage plant is extremely small (e.g., due to insect damage) relative to the field of view of the instrument, its reflectance curve will approach the spectrum for bare soil. These spectral behaviors are the basis for the quantification of cabbage plant parameters if reflectance spectra are used. 3.2. Aerial photograph and descriptive statistics of plant parameters and photo-derived variables Fig. 2 shows the digitized CIR photograph of the cabbage field. Differences in plant stand and sizes among the 81 experimental plots can be visually seen from the image. Individual cabbage plants can be separated within
the plots, especially in plots that had poor stand. On the CIR image, cabbage plants showed a dark red tone, while the sorghum plants (wind breaks) had a pinkish color. Soil background had a light grayish to dark grayish color. The aerial photograph provided a continuous view of the cabbage field and allowed quick visual comparisons among the experimental plots. Moreover, it contained digital spectral information for every area (pixel) of the field and allowed quantitative separation of the plants. Table 2 presents the descriptive statistics of the four cabbage plant physical parameters as well as plant area and seven spectral variables derived from the digitized aerial CIR photograph for the 243 cabbage plants. Plant diameter varied from 0.13 to 0.73 m with a mean of 0.43 m and a standard deviation (SD) of 0.13 m, while head diameter ranged from 0.05 to 0.22 m with a mean of 0.14 m and a SD of 0.04 m. The mean head diameter was approximately onethird of the mean plant diameter. Plant weight varied from 0.09 to 4.7 kg with a mean of 1.64 kg, while head weight varied from 0.06 to 3.80 kg with a mean of 1.41 kg, which was about 86% of the mean plant weight. Compared with plant diameter and head diameter, plant weight and head weight had much larger coefficients of variation. Plant area derived from the image had a mean of 0.162 m2, which is equivalent to a circle diameter of 0.45 m. Clearly, the photoderived mean plant diameter was very similar to the groundmeasured mean plant diameter. Mean digital count values were 152, 105 and 112 for the NIR, red and green bands, respectively. The coefficients of variation for the three bands were similar to those for the two band ratios, NR and NG, but were much smaller than those for the two normalized differences, NDVI and GNDVI.
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Fig. 2. A digitized aerial color-infrared photograph of a cabbage field with 81 experimental plots in south Texas in 2004. Each plot consisted of two rows of cabbage plants (dark red) and the plots were separated by two rows of grain sorghum plants as wind break (pink).
Table 2 Descriptive statistics of cabbage plant physical parameters measured on ground, and plant area and spectral variables derived from an aerial color-infrared photograph based on 243 cabbage plants from a cabbage field in south Texas Variable
Mean
Standard deviation
Coefficient of variation (%)
Minimum
Maximum
PD (m) HD (m) PW (kg) HW (kg) PA (m2) NIR Red Green NR NG NDVI GNDVI
0.43 0.14 1.64 1.41 0.162 152 105 112 1.48 1.37 0.183 0.151
0.13 0.04 0.95 0.82 0.063 23 22 22 0.27 0.20 0.093 0.075
29 27 58 58 38 15 21 20 18 15 51 50
0.13 0.05 0.09 0.06 0.032 99 68 70 0.86 0.89 0.077 0.060
0.73 0.22 4.70 3.80 0.357 204 176 175 2.04 1.84 0.342 0.295
PD ¼ plant diameter, HD ¼ head diameter, PW ¼ plant weight, HW ¼ head weight, PA ¼ plant area, NR ¼ NIR/red, NG ¼ NIR/green, NDVI ¼ (NIRred)/(NIR+red), and GNDVI ¼ (NIRgreen)/(NIR+green).
Table 3 Correlation coefficient matrix among cabbage plant physical parameters measured on ground and plant area and spectral variables derived from an aerial color-infrared photograph for a cabbage field in south Texas Plant physical parameter
HD PW HW PA NIR Red Green NR NG NDVI GNDVI
Photo-derived plant area and spectral variable
PD
HD
PW
HW
PA
NIR
Red
Green
NR
NG
NDVI
0.93 0.92 0.91 0.94 0.34 0.55 0.39 0.87 0.83 0.87 0.83
0.95 0.95 0.93 0.33 0.54 0.38 0.84 0.80 0.84 0.81
0.99 0.96 0.33 0.51 0.36 0.82 0.78 0.81 0.77
0.95 0.30 0.52 0.36 0.81 0.77 0.80 0.76
0.32 0.55 0.39 0.85 0.81 0.84 0.81
0.45 0.64 0.30 0.18 0.30 0.19
0.97 0.69 0.76 0.70 0.76
0.53 0.63 0.53 0.63
0.98 0.99 0.98
0.97 0.99
0.98
PD ¼ plant diameter, HD ¼ head diameter, PW ¼ plant weight, HW ¼ head weight, PA ¼ plant area, NR ¼ NIR/red, NG ¼ NIR/green, NDVI ¼ (NIRred)/(NIR+red), and GNDVI ¼ (NIRgreen)/(NIR+green). All r values with a magnitude of at least 0.25 are significant at the 0.0001 level. The number of samples was 243.
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3.3. Relationships among plant parameters and photoderived variables
The three spectral bands and four vegetation indices were all significantly related to each of the four plant physical parameters and plant area, even though the correlations were not as strong as those among the plant physical parameters and plant area. The green and red bands were negatively related to the plant physical parameters and plant area, while the NIR band and the vegetation indices were positively related to the plant variables. Moreover, the four vegetation indices had much higher r values than the three bands. Fig. 3 shows the scatter plots and best-fitting regression equations for four of the pairs among the four plant physical parameters. Head diameter was linearly related to plant diameter with an r2 value of 0.86. Plant weight was quadratically related to plant diameter with an r2 value of 0.89. Similarly, head weight was quadratically related to head diameter with an r2 value of 0.92. Head weight was
0.25
5
0.2
4
Plant weight (kg)
Head diameter (m)
Table 3 presents the correlation coefficient matrix among the plant physical parameters, plant area and spectral variables derived from the aerial CIR photograph for the 243 cabbage plants. The four plant physical parameters were significantly interrelated to one another with r values ranging from 0.91 between head weight and plant diameter to 0.99 between head weight and plant weight. Photo-derived plant area was also highly related to each of the four plant physical parameters with r values ranging from 0.93 for head diameter to 0.96 for plant weight. These results indicate that any of the plant physical parameters and plant area can be used to directly or indirectly measure cabbage head weight.
0.15
0.1
31
3
2
1
0.05
0
0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0
0.1
0.2
Plant diameter (m)
0.3
0.4
0.5
0.6
0.7
0.8
Plant diameter (m)
5
4
4
Head weight (kg)
Head weight (kg)
3
2
3
2
1 1
0
0 0
0.05
0.1
0.15
Head diameter (m)
0.2
0.25
0
1
2
3
4
5
Plant weight (kg)
Fig. 3. Scatter plots and regression equations between plant diameter (PD), head diameter (HD), plant weight (PW), and head weight (HW) based on 243 cabbage plants from a cabbage field in south Texas.
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linearly related to plant weight with an r2 value of 0.99. The intercept of the head weight–plant weight line is 0.01 (close to 0) and the slope of the line is 0.86, indicating the head weight was 86% of the plant weight for this cabbage variety at harvest. Fig. 4a shows the scatter plot and regression equation between head weight and photo-derived plant area. Head weight was linearly related with plant area with an r2 value of 0.91, indicating cabbage head weight can be accurately estimated from plant area derived from the aerial photograph. Fig. 4b shows the scatter plot and regression equation between head weight and NDVI. Head weight was quadratically related to NDVI with an r2 value of 0.66. The regression results for relating head weight to each of the other three vegetation indices and to all three bands are presented in Table 4. Compared with plant area, the photoderived spectral bands and vegetation indices were not as accurate for head weight estimation. 3.4. Relationships between head weight and field reflectance spectra
Table 4 Regression results for relating cabbage head weight to each of the four vegetation indices and to three spectral bands derived from an aerial colorinfrared photograph based on 243 cabbage plants on a cabbage field in south Texas Spectral variable
Regression equationa
Model r2 or R2
NRb NG NDVI GNDVI NIR, red, green
HW ¼ 0.53+0.860 NR2 HW ¼ 2.88+3.120 NG HW ¼ 0.30+2.822 NDVI+13.953 NDVI2 HW ¼ 0.14+8.385 GNDVI2 HW ¼ 0.97+0.024 NIR–0.030 red
0.662 0.591 0.663 0.583 0.629
a A quadratic model was applied to each of the four vegetation indices and stepwise regression was used to determine the significant terms in the quadratic model. Stepwise regression was used to relate head weight to the NIR, red and green bands. The best-fitting models and all variables or terms remaining in the models were significant at the 0.001 level. The number of samples was 243. b HW ¼ head weight, NR ¼ NIR/red, NG ¼ NIR/green, NDVI ¼ (NIRred)/(NIR+red), and GNDVI ¼ (NIRgreen)/(NIR+green).
5
5
4
4 Head weight (kg)
Head weight (kg)
Table 5 summarizes the stepwise regression results for relating cabbage head weight to the 601 bands from 400 to 1000 nm in the field reflectance spectra taken from the 243 cabbage plants. Eight spectral bands were identified to be significant at the 0.001 level in the best-fitting regression equation. The eight-band model explained 71% of the variability in head weight. The best-fitting one- to sevenband models accounted for 40% to 69% of the variability. The best single band was 760 nm, accounting for 40% of the variability in head weight. The best two-band combination was 847 and 893 nm, explaining 57% of the variability. Interestingly, the best single band was not included in the best two-band combination. The best
three-band combination explained 60% of the variability. As more bands were added, the R2 values increased and some of the bands significant in the previous models were replaced by new bands. Eventually, eight bands were included in the best-fitting equation and no other band could be added to the model to be significant at the 0.001 level. Fig. 5 shows the scatter plot between measured head weight and estimated head weight based on the eight-band regression model. Compared with the photo-derived spectral variables, the field reflectance spectra accounted for more variability in head weight, but still did not explain as much as the photo-derived plant area.
3
2
1
3
2
1
0
0 0
0.1
0.2 Plant area (m2)
0.3
0.4
-0.1
0
0.1
0.2
0.3
0.4
NDVI
Fig. 4. Scatter plots and regression equations relating head weight (HW) to photo-derived plant area (PA) and to NDVI based on 243 cabbage plants from a cabbage field in south Texas.
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Table 5 Stepwise regression results for relating cabbage head weight to all 601 bands between 400–1000 nm in the field spectra taken from 243 cabbage plants on a cabbage field in south Texas No. of bands
Regression equationa
Partial R2
Model R2
1 2 3 4 5 6 7 8
HWb ¼ 1.36+7.37 B760c HW ¼ 0.63+105.40 B847100.92 B893 HW ¼ 1.5989.67 B405+188.49 B511100.57 B603 HW ¼ 1.1159.60 B421+43.69 B511+118.65 B848115.82 B893 HW ¼ 1.2160.27 B409+125.06 B52075.71 B563+93.25 B84789.20 B893 HW ¼ 1.0658.10 B408+148.35 B525363.51 B563+263.34 B565+85.27 B84781.79 B893 HW ¼ 1.5956.90 B408+135.15 B525400.87 B563+312.88 B565+105.92 B84778.92 B89324.80 B904 HW ¼ 1.3368.12 B415+157.82 B525911.57 B563+809.03 B564+107.26 B84798.96 B89355.52 B905+50.76 B907
0.403 0.164 0.033 0.043 0.026 0.012 0.011 0.015
0.403 0.567 0.600 0.643 0.669 0.681 0.692 0.707
a
The best fitting one-, two-, y, and eight-variable models and all variables remaining in the models were significant at the 0.001 level. No other variable could be added to the 8-variable model to be significant at the 0.001 level. The number of samples was 243. b HW ¼ head weight. c B760 stands for band reflectance at a wavelength of 760 nm.
4
Table 6 Comparisons of means for cabbage yield estimated from a digitized CIR aerial photo among 16 insecticide treatments (in descending order with yield) in a field in south Texas
Measured head weight (kg)
3
2
Treatment ID
Treatment
Yielda (kg/ha)
T8 T10 T1 T7 T5
Spinosad 2SC l-Cyhalothrin 1EC Novaluron 0.83EC Indoxacarb 30WDG Novaluron 0.83EC alt. with l-cyhalothrin 1EC Novaluron 0.83EC alt. with Indoxacarb 30WDG Novaluron 0.83EC Spinosad 2SC alt. with Indoxacarb 30WDG Novaluron 0.83EC alt. with Spinosad 2SC ABG 6406 (Bt) QRD 6047 75% ABG 6405 (Bt) Spinosad 80% ABG 6064 (Bt) QRD 6047 50% Untreated control
71919a 62395ab 61115bc 61084bc 57212bc
T3 T2 T6
1
0 0
1
2
3
4
Estimated head weight (kg)
Fig. 5. Measured cabbage head weight versus estimated head weight based on eight significant spectral bands derived from field reflectance spectra of 243 cabbage plants from a cabbage field in south Texas.
3.5. Comparisons of insecticide treatments using estimated yield Table 6 shows the comparisons of means for cabbage yield estimated from the digitized CIR aerial photo among the 16 insecticide treatments including the untreated control. The ANOVA results and the LSD tests showed there existed significant differences among some of the treatments. Although this study was not an efficacy test, which would require data from multiple years and/or multiple sites, it did provide useful information about the performance of these treatments. More importantly, it illustrates how remote sensing can be used to assess the
T4 T14 T12 T13 T9 T15 T11 T16
56400bc 55869bc 55182bc 51654c 38508d 37602d 37540d 35698d 35604d 34074d 33044d
a Means followed by the same letter are not significantly different at the 0.05 level according to Fisher’s protected LSD following an analysis of variance on a randomized complete block design. Model effect: d.f. ¼ 18, F ¼ 12.37, Po0.0001; block effect: d.f. ¼ 3, F ¼ 4.60, P ¼ 0.0068; and treatment effect: d.f. ¼ 15, F ¼ 13.92, Po0.0001, LSD ¼ 9687 kg/ha.
efficacy of different insecticide treatments. The efficacy results will vary from year to year and from field to field, depending on the specific growing conditions, but the methods described can be used for cabbage in any year and for any field. 3.6. Practical considerations Among the three spectral methods, photo-derived plant area appeared to be the most accurate method for cabbage
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C. Yang et al. / Crop Protection 27 (2008) 25–35
yield estimation. In practice, photo-derived plant area for each plot can be directly used to represent the relative cabbage yield for the plot. Although total area for each plot can be determined by summing up the individual plant areas within the plot, image-processing techniques allow total plant area within a plot to be automatically determined. As for the cost of using aerial photography for cabbage yield estimation, each 23-cm CIR photo, which can cover one or more fields, costs approximately $15 for film and processing. The major cost will be the charge for flight mission that can run at least a few hundred dollars per flight, depending on the service to use and the distance to travel and other factors. However, if aerial photos are to be taken from a large number of fields during the same flight mission, the cost per field will be much lower. 4. Summary and conclusions This study illustrated that remote sensing techniques can be a useful tool for estimating cabbage plant physical parameters. Both aerial CIR photography and field reflectance spectra can be used to extract cabbage plant growth and yield information, but aerial photography is more effective and reliable for this application. Aerial photographs can be taken instantaneously over a large area, while it is time consuming to take a large number of reflectance spectra and difficult to collect the spectra from a field under the same measurement and weather conditions. Moreover, aerial photography allows qualitative visual evaluation of plant size differences caused by biotic and abiotic stresses. The results from this study show that plant physical parameters were more closely related to photo-derived plant area than to any of the photo-derived spectral bands and vegetation indices. Ground reflectance spectra provided better estimation of cabbage head weight than the photo-derived spectral variables, but were not as accurate and effective as the photo-derived plant area. Nevertheless, field reflectance spectra capture the spectral characteristics of plants in hundreds of narrow bands in the visible to NIR region of the spectrum and can be used to interpret aerial photographs and other imagery, while CIR photography only contains spectral data in three broad bands. Compared with traditional ground observation and measurement approaches, aerial photography is more effective and efficient if a large number of plots or treatments are to be evaluated over large fields. This is one of the first evaluations of remote sensing techniques for estimating cabbage plant growth parameters and the results were encouraging. More experiments are needed to evaluate this technology for mapping cabbage growth and yield variations and for assessing the efficacies of different insecticide treatments for controlling cabbage insects. Acknowledgments The authors thank Rene Davis (USDA-ARS, Weslaco, TX) for acquiring the aerial CIR photographs and Jim
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