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Using multispectral imagery to extract a pure spectral canopy signature for predicting peanut maturity
Computers and Electronics in Agriculture 162 (2019) 561–572
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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Original papers
Using multispectral imagery to extract a pure spectral canopy signature for predicting peanut maturity
T
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Hesham Abd-El Monsefa, Scot E. Smithb, , Diane L. Rowlandc, Nader Abd El Rasold a
Geology, Faculty of Science, Suez Canal University, Egypt Geomatics, University of Florida, United States c Agronomy, University of Florida, United States d University of Florida, United States b
A B S T R A C T
An Unmanned Autonomous Octocopter equipped with a multispectral camera was used to take imagery of peanut plant cover at different stages of maturity. Vegetation indexes (Normalized Difference Vegetation Index, Transformed Difference Vegetation Index, Modified Soil Adjusted Vegetation Index, Modified Chlorophyll Absorption Ratio Index and the Modified Triangular Vegetation Index) were stacked and used to mask out the peanut canopy cover from background soil, shadows and any other surficial materials. Masked peanut canopy was used to develop a peanut maturity-spectral reflectance prediction model. The model was built using partial least squares. Comparison between the model- predicted and real values showed that the model does not give an accurate estimate of maturity up to 60 days after planting, but the accuracy of the model increases with time. This may since the difference between chlorophyll a and become more significant in mature peanuts more than immature ones. The overall assessment of the model indicates that the model needs to be calibrated for more precise prediction of the peanut maturity. This could be achieved by expanding the peanut maturity, versus peanut leaf spectra, database by taking data more frequently, especially close to harvest. Data collection should be started two months after planting when the ratios between chlorophyll a and b become more detectable in the leaf reflectance spectra.
1. Introduction Peanut is among the most important oil and protein producing crops in the world. The United States produces one-third of the world peanuts, mostly in Georgia and Florida. In 2016, over 1,530,000 acres of peanuts were planted yielding over one million tons (National Agricultural Statistics Service, NASS, 2016). One of the most challenging aspects of peanut production is determining when to harvest. Harvesting too early or too late may reduce the quantity and quality of the crop. For more than 30 years, the Peanut Maturity Board method (PMB) has been widely used for deciding when to harvest peanuts (Rowland et al., 2008). The PMB relies on the color of the peanut’s mesocarp layer. As peanuts mature the mesocarp layer’s color changes from white to yellow, orange, brown and, finally, black. Hence, pod maturity can be determined by the mesocarp layer color. However, the PMB is time and effort consuming and requires a welltrained expert for accurate results. The constraints on the pod mesocarp method are extensively recorded in many literatures, for instance Carley et al. (2008), Rowland et al. (2006), Shahin et al. (2000) and Tollner et al. (1998). Several researchers utilized spectral analysis of peanut kernel or peanut canopy to evaluate peanut maturity. Wiliam (1978) employed
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airborne spectroradiometer to detect a red spectral shift in the chlorophyll absorption edge and, thus, to discriminate crop type and maturity. Machine vision was innovated and tested by Ghate et al. (1993) to measure pod maturity. Brent et al. (2016) tried to get a reflectance model for predicting soybean relative maturity and seed yield using canopy reflectance, they concluded that the performance of canopy reflectance models for soybean maturity and yield accounted for a significant portion of variability among genotypes for maturity in some environments and for seed yield in most environments. Indices such as the simple ratio (SR) and Normalized Difference Vegetation Index (NDVI) were used by Jordan (1969) and Rouse et al. (1973) respectively to relate ratio of near-infrared (NIR) reflectance to red (RED) reflectance to biomass. SR was found to have a strong correlation with biomass, leaf area index (LAI) and canopy cover (Hatfield, 1983; Wiegand et al., 1991; Ball and Konzak, 1993; Price and Bausch, 1995; Hatfield and Prueger, 2010). The NDVI has been used by other researchers such as Wiegand et al. (1991), Shanahan et al. (2001), Royo et al. (2003) and Marti et al. (2007) to predict yield and maturity of many crops using hyperspectral and satellite imagery. Shanahan et al. (2001) accurately predicted yields in corn in Nebraska making use of the Green NDVI (GNDVI). Hatfield and Prueger (2010) assessed the value of using distinct
Corresponding author. E-mail address: sesmith@ufl.edu (S.E. Smith).
https://doi.org/10.1016/j.compag.2019.04.028 Received 8 June 2018; Received in revised form 8 April 2019; Accepted 23 April 2019 Available online 09 May 2019 0168-1699/ © 2019 Elsevier B.V. All rights reserved.
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vegetation indices to quantify and characterize crop characteristics at different growth stages of corn, soybean, wheat, and canola. They used six different vegetation indices, these are SR, NDVI, PAR, SAVI, LAI and EVI. They found that these indices show differences during the growing season and these differences were a function of growth stage and vegetative index. These results clearly imply the need to use multiple vegetation indices to best capture agricultural crop characteristics. The objective of this research was to develop an alternative method using peanut canopy spectra to predict maturity.
Table 1 Spectral bands of the Rikola hyperspectral camera.
2. Methodology Chlorophyll type and content, concentration of nitrogen, phosphorus, potassium, magnesium, calcium, manganese, and iron in peanut leaves change at as plants mature (Rowland et al., 2008). Consequently, the peanuts canopy spectral characteristic can be expected to change as well. In this study, we measured changes in the peanut canopy spectral characteristics as peanut plants matured. Our goal was to develop a peanut maturity-spectral reflectance prediction model. The approach was as follows: – Use an Unmanned Autonomous Vehicle (UAV) octocopter to acquire multispectral images of the peanut canopy at different maturity stages. – Extract pure end-members that represent peanut maturity at different maturity stages – Build a peanut maturity-spectral reflectance prediction model using partial least square regression – Validate the model 2.1. Using a UAV octocopter to acquire multispectral images of peanut canopy at different maturity stages An octocopter equipped with a Rikola multispectral camera was used to take imagery of the peanut canopy at different maturity levels in July 2016. The dates corresponded with full canopy cover. The study site was the University of Florida’s Agricultural Research Center in Citra Florida. The Rikola is a frame-based spectral system providing snapshot images in the visible and near-infrared portion of the electromagnetic spectrum with a 12-bit dynamic spectral range. The camera was configured to capture images in 15 bands, each band with spectral width of 10 nm (Table 1). The results are shown in Fig. 1.
Days after plantation
Ratio 1
Ratio 2
Ratio 3
Ratio 4
70 70 70 70 70 70 70 70 70 90 90 90 90 90 90 90 90 90 110 110 110 110 110 110 110 110 110 120 120 120 120 120 120 120 120 120 130 130 130 130 130 130 130 130 130
0.817 0.781 0.867 0.871 0.786 0.844 0.892 0.815 0.782 0.846 0.833 0.745 0.862 0.842 0.822 0.801 0.911 0.719 0.826 0.793 0.906 0.699 0.802 0.782 0.761 0.753 0.758 0.799 0.688 0.622 0.611 0.841 0.602 0.759 0.771 0.662 0.771 0.544 0.511 0.589 0.654 0.678 0.459 0.587 0.661
0.878 0.717 0.778 0.776 0.716 0.756 0.735 0.727 0.732 0.755 0.699 0.754 0.771 0.661 0.731 0.697 0.702 0.797 0.634 0.721 0.841 0.675 0.873 0.711 0.689 0.599 0.586 0.662 0.646 0.684 0.621 0.651 0.511 0.665 0.689 0.455 0.584 0.599 0.756 0.589 0.711 0.618 0.587 0.552 0.427
0.396 0.393 0.416 0.552 0.532 0.412 0.501 0.463 0.501 0.417 0.495 0.417 0.433 0.414 0.493 0.372 0.365 0.437 0.399 0.387 0.399 0.415 0.396 0.375 0.354 0.347 0.395 0.389 0.401 0.511 0.322 0.366 0.366 0.411 0.345 0.368 0.377 0.399 0.381 0.406 0.321 0.301 0.399 0.478 0.374
0.308 0.495 0.307 0.214 0.284 0.184 0.393 0.155 0.416 0.191 0.188 0.201 0.317 0.197 0.277 0.256 0.248 0.353 0.188 0.172 0.188 0.199 0.109 0.165 0.143 0.136 0.141 0.178 0.211 0.182 0.222 0.116 0.161 0.221 0.112 0.132 0.176 0.194 0.178 0.198 0.122 0.122 0.131 0.214 0.122
• Normalized Difference Vegetation Index (NDVI) • Transformed Difference Vegetation Index (TDVI) • Modified Soil Adjusted Vegetation Index (MSAVI) • Modified Chlorophyll Absorption Ratio Index (MCARI) • Modified Triangular Vegetation Index (MTVI)
2.2. Extracting the pure end-member that represents peanut maturity at different maturity stages Precise identification of the spectral characteristics or the spectral signature of the peanut canopy at different maturity stages depends on the accuracy of “end-member” determination with end-member being an idealized, pure spectral signature. Pure end-members representing the canopy at each maturity stage were selected using the approach shown in Fig. 2.
The spectral bands acquired by the multispectral scanner were equivalent to the bands required by each index. They were selected to be in the center of the band ranges of the multispectral camera. By default, Rikola multispectral camera capture spectral radiance (mW/ [m2·str·nm]), then reflectance was calculated by defining a spectral reference. This is done by a special software that delivered with the camera.
2.2.1. Define the image pixels mostly occupied by peanut canopy Healthy green vegetation such as peanut canopy cover has a unique spectral signature making it distinctive on multispectral images. Green vegetation has relatively low reflectance in the visible part of the electromagnetic spectrum (400 to 700 nm) and relatively high reflectance at near infrared (700 to 900 nm). This is due to the absorption peaks of the chlorophyll at 450 nm (chlorophyll a) and 650 nm (chlorophyll b) and high reflectance caused by plant’s internal cell structure within the range of 700–900 nm. Based on this, the following vegetation indices were utilized to define pixels with high vegetation coverage:
2.2.1.1. The Normalized Difference Vegetation Index (NDVI). The NDVI determines and monitors plant growth, vegetation cover and biomass production from multispectral and multispectral imagery. It depends on the difference in the vegetation reflection in the red and the infrared band ranges (Rouse et al., 1973). The Ricola multispectral camera’s Red band 6 and NIR band 12 were used to apply the index as shown in Fig. 3):
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Fig. 1. Peanut Crop taken on 6 July 6 and 26 July 2016.
NDVI =
NIRb12 − Rb6 NIRb12 + Rb6
drawn between leaf reflection in the green, red and infrared bands. This area increases with increasing chlorophyll absorption in the red band, slightly increasing in the green band along with highly increasing of near-infrared reflectance (Hunt et al., 2011). It differs from the MCARI because it is more related to the leave structure than its green pigment content. To apply MTVI, the multispectral bands 2, 6 and 12, with center wavelengths of 545, 660 and 790 nm, respectively were used and are shown in Fig. 5.
2.2.1.2. Modified Chlorophyll Absorption Ratio Index (MCARI). MCARI is the depth of the chlorophyll absorption at 670 nm relative to the reflectance at 550 nm and 700 nm. MCARI is more related to chlorophyll content of the leave and minimizes the contribution soil makes to the signal (Daughtry et al., 2000). The results are shown in Fig. 4.
2.2.1.4. Soil-Adjusted Vegetation Index (SAVI). The soil-adjusted vegetation index (SAVI) was developed to minimize soil influences on canopy spectra by incorporating a soil adjustment factor L = 0.5 in the denominator of the normalized difference vegetation index (NDVI) equation (Huete, 1988). The results are shown in Fig. 6.
MCARI = [(Band10 − Band6) − (0.2 ∗ Band10 − Band2 )] ∗ (Band10 / Band6)
2.2.1.3. Modified Triangular Vegetation Index (MTVI). The theory behind the MTVI relies on measuring the total area of a triangle 563
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Fig. 2. Approach to Extracting Pure End-Members that Best Represent Peanut Canopy at Different Maturity Stages.
SAVI = (1+L) ∗
– A spatial model of ERDAS Imagine software was designed to create a 0/1 mask layer based on the 5-layer vegetation indices image. A threshold equal to 90% of the maximum value of each layer was used. The pixel of mask layer assigned 1 when the same pixel in all the vegetation index layers passed the threshold, Otherwise, the pixel value was assigned 0 as shown in Fig. 8.
(Band12 − Band6 ) Band12 − Band6 + L
2.2.1.5. Transformed Difference Vegetation Index (TDVI). The TDVI index shows the same sensitivity as the Soil Adjusted Vegetation Index (SAVI) to the optical proprieties of bare soil subjacent to the vegetation cover, but it has the advantage that it does not saturate like the SAVI (Bannari et al., 2002). In addition, it shows a high linearity as a function of the rate of vegetation cover as shown in Fig. 7.
TDVI = 1.5 ∗
(Band12 − Band6 ) 2 Band12 − Band6 + 0.5
The pixel of DN = 1 was assumed to be the pixel mostly covered by vegetation, therefore the 0/1 image was used as a vegetation mask image. The vegetation mask image was used to mask out the vegetated area out of the multispectral image. The new vegetated masked image contained only the area mostly covered by vegetation as shown in Fig. 9.
In order to define image pixels that were mostly occupied by peanut canopy and to avoid pixels that were partially covered by soil and shadow, a vegetation mask layer was created as follows:
2.2.2. Using “growing method” of ENVI software to define the training sites The grow-pixel algorithm of ENVI image processing software was used to define training sites that represented the peanut cultivars. The algorithm was input with the vegetated masked image. For each cultivar plot, we select a seed pixel that 100% covered by peanut leaves
– Images of NDVI, MCARI, MTVI, SAVI and TDVI vegetation indices were stacked into one 5-layer vegetation indices image. 564
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Fig. 3. NDVI of Peanut Canopy using Multispectral Images taken on 6 and 26 July 2016.
Fig. 4. MCARI of Peanut Canopy using Multispectral Imagery taken on 6 and 26 July 2016. 565
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Fig. 5. MTVI of Peanut Canopy using Multispectral Imagery taken on 6 and 26 July 2016.
Fig. 6. SAVI of Peanut Canopy using UAV imagery taken on 6 and 26 July 2016. 566
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Fig. 7. TDVI of Peanut Canopy using Multispectral Imagery taken on 6 and 26 July 2016.
routine written in Interactive Data Language (IDL) and run under ENVI software. VIPER uses the end-member’s average (EAR) root mean square error to identify which spectra are most representative of a peanut’s maturity level. EAR is calculated as the average root mean square error (RMSE) produced by a spectrum when it is used to model all other spectra of the same peanut maturity level. The calculation starts with the determination of the square array that retains all calculations of RMSE using the equation:
with zero fraction of shadows or soil contamination. The REGION_GROW function performs region growing for a given region within an N-dimensional array by finding all pixels within the array that are connected neighbors and have similar spectral characteristics to the seed pixel. Similarity to the spectral characteristic to the seed pixel is constrained by threshold range (a minimum and maximum pixel value). The threshold range for each peanut cultivar was defined by define the mean pixel value of each band as shown by the masked vegetated image plus and minus one stander deviation. The region is grown to include all connected neighboring pixels that fall within the given threshold range. This region was used as a training site of the cultivar plot (Fig. 10).
λ
RMSE =
1/2
⎧ ∑ (εiλ )2/N ⎫⎬ ⎨ k=1 ⎩ ⎭
where N is the number of spectra measured for one training site and λ is the total number of bands. In this instance, the optimum spectrum would be the one that produces the lowest average root mean square error. Columns and rows store the model number, corresponding to one column and row for each spectrum segment
2.2.3. Extracting the peanut canopy end-members Pixels of each training site were mathematically evaluated to define the pixels with the most representative spectral signatures for the training sites. They were, by definition, end-members. The pure end-members that best represented each training site were determined by using the VIPER tool. VIPER is a computer software
Fig. 8. Vegetation Mask. 567
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Fig. 9. Pixels Occupied Mostly by Peanut Canopy.
that, at this band, mature peanuts have more absorption than younger ones. This is due to an increase in chlorophyll a to chlorophyll b ratio. The chlorophyll a to chlorophyll b ratio increased with increasing peanut maturity (Banks and Eskins, 1981). The same phenomenon occurs at band 8 (690–700 nm), but to a lesser extent.
2.3. Building peanut leaf spectra-peanut maturity level mathematical PLS model Several mathematical models were tested to calibrate the VNIR-NIRSpectral Reflectance of peanut maturity level end-members against the peanut maturity expressed as the number of days after planting. Partial least squares (PLS) is a method for constructing predictive models when there are many highly collinear factors. Partial least squares regression is an extension of the multiple linear regression model. In its simplest form, a linear model specifies the relationship between a dependent (response) variable Y, and a set of predictor variables, the X's:
Therefore, bands 6 and 8 are good indicators for peanut maturity. The modification in leaf reflectance can be analyzed by using these two bands, but results will be more noticeable when information is combined by calculating band ratios. Therefore, end-member spectra were used to calculate the following vegetation ratios based on bands 6 and 8. These ratios were used to build the PLS model instead of using the individual bands:
Y = c 0 + c1X1 + c 2X2 + ...+c p Xp Where,
– The Normalized Difference Vegetation Index (NDVI): two NDVI ratios were calculated, one use Rikola Red band 6 and the other used the Red band 8, in addition to Rikola NIR band 12. – Normalized Difference Greenness Index (NDGI): this ratio was firstly used by Chamard et al. (1991). Two NDGI ratios were calculated one use Rikola Red band 6 and the other used the Red band 8, in addition to Rikola Green band 2.
c0 is the regression coefficient for the intercept ci are the regression coefficients (for variables 1 through p) computed from the data The partial least squares model of Origin software was populated with the values of the four vegetation indices as a set of predictor variables and the corresponding values of Days After Planting (DAP) as dependent variables. 3. Results and discussion By comparing the end-member spectral signatures of different maturity stages as shown in Fig. 11. It was found that: – At near-infrared (NIR) ranges, no significant differences existed between the peanuts spectral signatures at regardless of maturity stage – At visible (VIS) ranges, the blue, green and red bands were primarily associated with the total chlorophyll content (chlorophyll a and b), but, total chlorophyll content became constant at a specific stage of maturity (Rowland et al., 2008). This explains the similarity of the spectra of peanuts of different maturity levels in the green band – There is a high absorption peak at the red band 6 (655–665 nm) due to the high absorption of chlorophyll a at this band. It was noticed
NDVI 1 =
b12 − b6 b12 + b6
NDVI 2 =
b12 − b8 b12 + b8
NDGI 1 =
b2 − b6 b2 + b6
NDGI 2 =
b 2 − b8 b 2 + b8
Where, b2 is the green band centered at 545 nm b6 is the red band centered at 660 nm b8 is the Red band centered at 695 nm b12 is the NIR band centered at 785 nm
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Fig. 10. Training sites (in red) for the 6 July 2016 imagery.
c2 = −36.31639 c3 = −90.93535 c4 = −95.04471
The ratios were calculated for the spectra of all the training sites collected for various maturity stages as shown in Table 2 and used as input for the PLS regression module. Ratios were used as PLS independent variables (predicators) and maturity stages (expressed with the number of days after planting) were used as the dependent variable. The partial least square prediction model is expressed with the following equation:
Diagnostics plots are residual plots of Y and X as shown in Fig. 12. They were used to judge the quality of the model. Overall, it can be said that the peanuts leaf cover spectra-peanut maturity level mathematical PLS model is reasonable because:
DaysafterPeanutPlanting = c0 + (c1 ∗ NDGI1) + (c2 ∗ NDVI1)
– The predicted values-actual values graph indicates that the model fit well the first component – In the predicted values-residual graph, residuals are randomly distributed around zero indicating that there is no drift – The probability plot of residual shows that the result falls in a line, which means the variance is normally distributed
+ (c3 ∗ NDGI2) + (c4 ∗ NDVI2) Where, c0 = 250.22419 c1 = −85.90345 569
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Fig. 11. Comparison between End-member Spectral Signatures at Different Maturity Stages (DAP = 70 in red and DAP = 90 in black).
The model was constructed with data taken in 2017. Comparison between the model- predicted and real values, presented in Table 4, shows that the model does not give an accurate estimation in the first two months since planting, but accuracy of the model increases with time. This may due to the fact that the difference between chlorophyll a and b become more significant in mature peanuts more than immature plants. The overall assessment of the model indicates that the model needs to be calibrated for more precise prediction of the peanut maturity. This could be achieved by expanding the peanut maturity, versus peanut leaf spectra, database by taking data more frequently, especially close to harvest. Data collection should be started two months after planting when the ratios between chlorophyll a and b become more detectable in the leaf reflectance spectra.
Table 2 Data used as input to the PLS regression model. Color
Band Number
Spectral Range(nm)
Green
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
500–510 540–550 570–580 600–610 620–630 655–665 680–690 690–700 700–710 710–720 720–730 780–790 830–840 860–870 885–895
Red
Red Edge
Near Infrared
4. Conclusions Comparison between the model- predicted and real values showed that the model does not give an accurate estimate of maturity up to 60 days after planting, but the accuracy of the model increases with time. This may due to the fact that the difference between chlorophyll a and b become more significant in mature peanuts more than immature ones. The overall assessment of the model indicates that the model needs to be calibrated for more precise prediction of the peanut maturity. This could be achieved by expanding the peanut maturity, versus peanut leaf spectra, database by taking data more frequently, especially close to harvest. Data collection should be started two months after planting when the ratios between chlorophyll a and b become more detectable in the leaf reflectance spectra. Once properly calibrated, this approach to predicting peanut
A summary, the significance of each one of the vegetation indices within the prediction model is given in the VIP plot. The plot demonstrates that RGBVI is the most significant compared to the other indices, while the TGI index is the least as shown in Fig. 13. The model presented here was built using data collected in 2016. In order to validate the model, multispectral camera imagery was used to take images of the same peanut field one year later. The images were taken using the same multispectral scanner and the same spectral bands used to build the PLS model. These images were taken 48, 60, 80 and 121 days after planting. The ratios 1, 2, 3, and 4 were calculated for the spectral signatures of training sites selected randomly in the peanut field in 2017 and are shown in Table 3. 570
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Fig. 12. PLS diagnostic plots. Table 3 Data used for model validation.
Fig. 13. VIP plot of the independent variables of the peanut leave reflectancewater stress prediction model.
maturity would be an extremely valuable tool for farmers. The approach is quantitative compared with the more subjective peanut board and much for efficient.
Training site
Days after plantation
Ratio 1
Ratio 2
Ratio 3
Ratio 4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
48 48 48 48 48 48 60 60 60 60 60 60 80 80 80 80 80 80 120 120 120 120 120 120
0.817 0.798 0.791 0.787 0.865 0.813 0.717 0.769 0.767 0.701 0.686 0.803 0.8315 0.807 0.806 0.8665 0.814 0.833 0.701 0.627 0.639 0.643 0.791 0.617
0.722 0.658 0.727 0.689 0.816 0.776 0.699 0.617 0.708 0.786 0.811 0.721 0.8165 0.708 0.766 0.7735 0.6885 0.7435 0.618 0.646 0.684 0.521 0.731 0.511
0.382 0.424 0.389 0.374 0.402 0.393 0.391 0.403 0.411 0.362 0.502 0.389 0.4065 0.444 0.4165 0.4925 0.473 0.4525 0.389 0.401 0.511 0.322 0.366 0.366
0.311 0.376 0.361 0.234 0.387 0.397 0.218 0.394 0.345 0.204 0.354 0.394 0.2495 0.3415 0.254 0.2655 0.2405 0.2305 0.205 0.201 0.172 0.122 0.129 0.171
Appendix A. Supplementary material Acknowledgement
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compag.2019.04.028.
We wish to acknowledge to support of the National Peanut Board for funding this research. 571
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Table 4 Comparison between predicted and actual maturity dates. Predict values
Real days after plantation
Error %
Training sites
Days after plantation
Average predicted days after plantation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
80.9894 80.10834 88.07043 96.75214 73.98829 77.49265 101.56597 88.55243 85.8856 97.84083 76.07689 83.98884 86.83941 84.27307 90.42956 77.3494 90.34679 88.39733 113.73493 115.83024 103.74882 137.40367 108.96785 131.31196
83
48
73%
89
60
48%
86
80
7.5%
118
121
2.4%
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References Ball, S., Konzak, C., 1993. Relationship between grain-yield and remotely-sensed data in wheat breeding experiments. Plant Breed 110 (4), 277–282. Bannari A., K. Asalhi and P. Teillet P., 2002, Transformed difference vegetation index for vegetation cover mapping. In: Proceedings of the International Geoscience and Remote Sensing Symposium, Toronto, Ontario, Canada, Paper I2A35-1508. Banks, D., Eskins, K., 1981. Analysis of normal and mutant peanut chloroplast pigments by liquid chromatography. Peanut Sci. 8, 40–42. Brent, S., William, T., Nan, A., Kevin, P., Vara, P., Allan, K., 2016. Predicting soybean relative maturity and seed yield using canopy reflectance. Science 56, 625–643. Carley, D., Jordan, D., Dharmasri, L., Sutton, T., Brandenburg, R., Burton, M., 2008. Peanut response to planting date and potential of canopy reflectance as an indicator
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Contents lists available at ScienceDirect
Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Original papers
Using multispectral imagery to extract a pure spectral canopy signature for predicting peanut maturity
T
⁎
Hesham Abd-El Monsefa, Scot E. Smithb, , Diane L. Rowlandc, Nader Abd El Rasold a
Geology, Faculty of Science, Suez Canal University, Egypt Geomatics, University of Florida, United States c Agronomy, University of Florida, United States d University of Florida, United States b
A B S T R A C T
An Unmanned Autonomous Octocopter equipped with a multispectral camera was used to take imagery of peanut plant cover at different stages of maturity. Vegetation indexes (Normalized Difference Vegetation Index, Transformed Difference Vegetation Index, Modified Soil Adjusted Vegetation Index, Modified Chlorophyll Absorption Ratio Index and the Modified Triangular Vegetation Index) were stacked and used to mask out the peanut canopy cover from background soil, shadows and any other surficial materials. Masked peanut canopy was used to develop a peanut maturity-spectral reflectance prediction model. The model was built using partial least squares. Comparison between the model- predicted and real values showed that the model does not give an accurate estimate of maturity up to 60 days after planting, but the accuracy of the model increases with time. This may since the difference between chlorophyll a and become more significant in mature peanuts more than immature ones. The overall assessment of the model indicates that the model needs to be calibrated for more precise prediction of the peanut maturity. This could be achieved by expanding the peanut maturity, versus peanut leaf spectra, database by taking data more frequently, especially close to harvest. Data collection should be started two months after planting when the ratios between chlorophyll a and b become more detectable in the leaf reflectance spectra.
1. Introduction Peanut is among the most important oil and protein producing crops in the world. The United States produces one-third of the world peanuts, mostly in Georgia and Florida. In 2016, over 1,530,000 acres of peanuts were planted yielding over one million tons (National Agricultural Statistics Service, NASS, 2016). One of the most challenging aspects of peanut production is determining when to harvest. Harvesting too early or too late may reduce the quantity and quality of the crop. For more than 30 years, the Peanut Maturity Board method (PMB) has been widely used for deciding when to harvest peanuts (Rowland et al., 2008). The PMB relies on the color of the peanut’s mesocarp layer. As peanuts mature the mesocarp layer’s color changes from white to yellow, orange, brown and, finally, black. Hence, pod maturity can be determined by the mesocarp layer color. However, the PMB is time and effort consuming and requires a welltrained expert for accurate results. The constraints on the pod mesocarp method are extensively recorded in many literatures, for instance Carley et al. (2008), Rowland et al. (2006), Shahin et al. (2000) and Tollner et al. (1998). Several researchers utilized spectral analysis of peanut kernel or peanut canopy to evaluate peanut maturity. Wiliam (1978) employed
⁎
airborne spectroradiometer to detect a red spectral shift in the chlorophyll absorption edge and, thus, to discriminate crop type and maturity. Machine vision was innovated and tested by Ghate et al. (1993) to measure pod maturity. Brent et al. (2016) tried to get a reflectance model for predicting soybean relative maturity and seed yield using canopy reflectance, they concluded that the performance of canopy reflectance models for soybean maturity and yield accounted for a significant portion of variability among genotypes for maturity in some environments and for seed yield in most environments. Indices such as the simple ratio (SR) and Normalized Difference Vegetation Index (NDVI) were used by Jordan (1969) and Rouse et al. (1973) respectively to relate ratio of near-infrared (NIR) reflectance to red (RED) reflectance to biomass. SR was found to have a strong correlation with biomass, leaf area index (LAI) and canopy cover (Hatfield, 1983; Wiegand et al., 1991; Ball and Konzak, 1993; Price and Bausch, 1995; Hatfield and Prueger, 2010). The NDVI has been used by other researchers such as Wiegand et al. (1991), Shanahan et al. (2001), Royo et al. (2003) and Marti et al. (2007) to predict yield and maturity of many crops using hyperspectral and satellite imagery. Shanahan et al. (2001) accurately predicted yields in corn in Nebraska making use of the Green NDVI (GNDVI). Hatfield and Prueger (2010) assessed the value of using distinct
Corresponding author. E-mail address: sesmith@ufl.edu (S.E. Smith).
https://doi.org/10.1016/j.compag.2019.04.028 Received 8 June 2018; Received in revised form 8 April 2019; Accepted 23 April 2019 Available online 09 May 2019 0168-1699/ © 2019 Elsevier B.V. All rights reserved.
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vegetation indices to quantify and characterize crop characteristics at different growth stages of corn, soybean, wheat, and canola. They used six different vegetation indices, these are SR, NDVI, PAR, SAVI, LAI and EVI. They found that these indices show differences during the growing season and these differences were a function of growth stage and vegetative index. These results clearly imply the need to use multiple vegetation indices to best capture agricultural crop characteristics. The objective of this research was to develop an alternative method using peanut canopy spectra to predict maturity.
Table 1 Spectral bands of the Rikola hyperspectral camera.
2. Methodology Chlorophyll type and content, concentration of nitrogen, phosphorus, potassium, magnesium, calcium, manganese, and iron in peanut leaves change at as plants mature (Rowland et al., 2008). Consequently, the peanuts canopy spectral characteristic can be expected to change as well. In this study, we measured changes in the peanut canopy spectral characteristics as peanut plants matured. Our goal was to develop a peanut maturity-spectral reflectance prediction model. The approach was as follows: – Use an Unmanned Autonomous Vehicle (UAV) octocopter to acquire multispectral images of the peanut canopy at different maturity stages. – Extract pure end-members that represent peanut maturity at different maturity stages – Build a peanut maturity-spectral reflectance prediction model using partial least square regression – Validate the model 2.1. Using a UAV octocopter to acquire multispectral images of peanut canopy at different maturity stages An octocopter equipped with a Rikola multispectral camera was used to take imagery of the peanut canopy at different maturity levels in July 2016. The dates corresponded with full canopy cover. The study site was the University of Florida’s Agricultural Research Center in Citra Florida. The Rikola is a frame-based spectral system providing snapshot images in the visible and near-infrared portion of the electromagnetic spectrum with a 12-bit dynamic spectral range. The camera was configured to capture images in 15 bands, each band with spectral width of 10 nm (Table 1). The results are shown in Fig. 1.
Days after plantation
Ratio 1
Ratio 2
Ratio 3
Ratio 4
70 70 70 70 70 70 70 70 70 90 90 90 90 90 90 90 90 90 110 110 110 110 110 110 110 110 110 120 120 120 120 120 120 120 120 120 130 130 130 130 130 130 130 130 130
0.817 0.781 0.867 0.871 0.786 0.844 0.892 0.815 0.782 0.846 0.833 0.745 0.862 0.842 0.822 0.801 0.911 0.719 0.826 0.793 0.906 0.699 0.802 0.782 0.761 0.753 0.758 0.799 0.688 0.622 0.611 0.841 0.602 0.759 0.771 0.662 0.771 0.544 0.511 0.589 0.654 0.678 0.459 0.587 0.661
0.878 0.717 0.778 0.776 0.716 0.756 0.735 0.727 0.732 0.755 0.699 0.754 0.771 0.661 0.731 0.697 0.702 0.797 0.634 0.721 0.841 0.675 0.873 0.711 0.689 0.599 0.586 0.662 0.646 0.684 0.621 0.651 0.511 0.665 0.689 0.455 0.584 0.599 0.756 0.589 0.711 0.618 0.587 0.552 0.427
0.396 0.393 0.416 0.552 0.532 0.412 0.501 0.463 0.501 0.417 0.495 0.417 0.433 0.414 0.493 0.372 0.365 0.437 0.399 0.387 0.399 0.415 0.396 0.375 0.354 0.347 0.395 0.389 0.401 0.511 0.322 0.366 0.366 0.411 0.345 0.368 0.377 0.399 0.381 0.406 0.321 0.301 0.399 0.478 0.374
0.308 0.495 0.307 0.214 0.284 0.184 0.393 0.155 0.416 0.191 0.188 0.201 0.317 0.197 0.277 0.256 0.248 0.353 0.188 0.172 0.188 0.199 0.109 0.165 0.143 0.136 0.141 0.178 0.211 0.182 0.222 0.116 0.161 0.221 0.112 0.132 0.176 0.194 0.178 0.198 0.122 0.122 0.131 0.214 0.122
• Normalized Difference Vegetation Index (NDVI) • Transformed Difference Vegetation Index (TDVI) • Modified Soil Adjusted Vegetation Index (MSAVI) • Modified Chlorophyll Absorption Ratio Index (MCARI) • Modified Triangular Vegetation Index (MTVI)
2.2. Extracting the pure end-member that represents peanut maturity at different maturity stages Precise identification of the spectral characteristics or the spectral signature of the peanut canopy at different maturity stages depends on the accuracy of “end-member” determination with end-member being an idealized, pure spectral signature. Pure end-members representing the canopy at each maturity stage were selected using the approach shown in Fig. 2.
The spectral bands acquired by the multispectral scanner were equivalent to the bands required by each index. They were selected to be in the center of the band ranges of the multispectral camera. By default, Rikola multispectral camera capture spectral radiance (mW/ [m2·str·nm]), then reflectance was calculated by defining a spectral reference. This is done by a special software that delivered with the camera.
2.2.1. Define the image pixels mostly occupied by peanut canopy Healthy green vegetation such as peanut canopy cover has a unique spectral signature making it distinctive on multispectral images. Green vegetation has relatively low reflectance in the visible part of the electromagnetic spectrum (400 to 700 nm) and relatively high reflectance at near infrared (700 to 900 nm). This is due to the absorption peaks of the chlorophyll at 450 nm (chlorophyll a) and 650 nm (chlorophyll b) and high reflectance caused by plant’s internal cell structure within the range of 700–900 nm. Based on this, the following vegetation indices were utilized to define pixels with high vegetation coverage:
2.2.1.1. The Normalized Difference Vegetation Index (NDVI). The NDVI determines and monitors plant growth, vegetation cover and biomass production from multispectral and multispectral imagery. It depends on the difference in the vegetation reflection in the red and the infrared band ranges (Rouse et al., 1973). The Ricola multispectral camera’s Red band 6 and NIR band 12 were used to apply the index as shown in Fig. 3):
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Fig. 1. Peanut Crop taken on 6 July 6 and 26 July 2016.
NDVI =
NIRb12 − Rb6 NIRb12 + Rb6
drawn between leaf reflection in the green, red and infrared bands. This area increases with increasing chlorophyll absorption in the red band, slightly increasing in the green band along with highly increasing of near-infrared reflectance (Hunt et al., 2011). It differs from the MCARI because it is more related to the leave structure than its green pigment content. To apply MTVI, the multispectral bands 2, 6 and 12, with center wavelengths of 545, 660 and 790 nm, respectively were used and are shown in Fig. 5.
2.2.1.2. Modified Chlorophyll Absorption Ratio Index (MCARI). MCARI is the depth of the chlorophyll absorption at 670 nm relative to the reflectance at 550 nm and 700 nm. MCARI is more related to chlorophyll content of the leave and minimizes the contribution soil makes to the signal (Daughtry et al., 2000). The results are shown in Fig. 4.
2.2.1.4. Soil-Adjusted Vegetation Index (SAVI). The soil-adjusted vegetation index (SAVI) was developed to minimize soil influences on canopy spectra by incorporating a soil adjustment factor L = 0.5 in the denominator of the normalized difference vegetation index (NDVI) equation (Huete, 1988). The results are shown in Fig. 6.
MCARI = [(Band10 − Band6) − (0.2 ∗ Band10 − Band2 )] ∗ (Band10 / Band6)
2.2.1.3. Modified Triangular Vegetation Index (MTVI). The theory behind the MTVI relies on measuring the total area of a triangle 563
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Fig. 2. Approach to Extracting Pure End-Members that Best Represent Peanut Canopy at Different Maturity Stages.
SAVI = (1+L) ∗
– A spatial model of ERDAS Imagine software was designed to create a 0/1 mask layer based on the 5-layer vegetation indices image. A threshold equal to 90% of the maximum value of each layer was used. The pixel of mask layer assigned 1 when the same pixel in all the vegetation index layers passed the threshold, Otherwise, the pixel value was assigned 0 as shown in Fig. 8.
(Band12 − Band6 ) Band12 − Band6 + L
2.2.1.5. Transformed Difference Vegetation Index (TDVI). The TDVI index shows the same sensitivity as the Soil Adjusted Vegetation Index (SAVI) to the optical proprieties of bare soil subjacent to the vegetation cover, but it has the advantage that it does not saturate like the SAVI (Bannari et al., 2002). In addition, it shows a high linearity as a function of the rate of vegetation cover as shown in Fig. 7.
TDVI = 1.5 ∗
(Band12 − Band6 ) 2 Band12 − Band6 + 0.5
The pixel of DN = 1 was assumed to be the pixel mostly covered by vegetation, therefore the 0/1 image was used as a vegetation mask image. The vegetation mask image was used to mask out the vegetated area out of the multispectral image. The new vegetated masked image contained only the area mostly covered by vegetation as shown in Fig. 9.
In order to define image pixels that were mostly occupied by peanut canopy and to avoid pixels that were partially covered by soil and shadow, a vegetation mask layer was created as follows:
2.2.2. Using “growing method” of ENVI software to define the training sites The grow-pixel algorithm of ENVI image processing software was used to define training sites that represented the peanut cultivars. The algorithm was input with the vegetated masked image. For each cultivar plot, we select a seed pixel that 100% covered by peanut leaves
– Images of NDVI, MCARI, MTVI, SAVI and TDVI vegetation indices were stacked into one 5-layer vegetation indices image. 564
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Fig. 3. NDVI of Peanut Canopy using Multispectral Images taken on 6 and 26 July 2016.
Fig. 4. MCARI of Peanut Canopy using Multispectral Imagery taken on 6 and 26 July 2016. 565
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Fig. 5. MTVI of Peanut Canopy using Multispectral Imagery taken on 6 and 26 July 2016.
Fig. 6. SAVI of Peanut Canopy using UAV imagery taken on 6 and 26 July 2016. 566
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Fig. 7. TDVI of Peanut Canopy using Multispectral Imagery taken on 6 and 26 July 2016.
routine written in Interactive Data Language (IDL) and run under ENVI software. VIPER uses the end-member’s average (EAR) root mean square error to identify which spectra are most representative of a peanut’s maturity level. EAR is calculated as the average root mean square error (RMSE) produced by a spectrum when it is used to model all other spectra of the same peanut maturity level. The calculation starts with the determination of the square array that retains all calculations of RMSE using the equation:
with zero fraction of shadows or soil contamination. The REGION_GROW function performs region growing for a given region within an N-dimensional array by finding all pixels within the array that are connected neighbors and have similar spectral characteristics to the seed pixel. Similarity to the spectral characteristic to the seed pixel is constrained by threshold range (a minimum and maximum pixel value). The threshold range for each peanut cultivar was defined by define the mean pixel value of each band as shown by the masked vegetated image plus and minus one stander deviation. The region is grown to include all connected neighboring pixels that fall within the given threshold range. This region was used as a training site of the cultivar plot (Fig. 10).
λ
RMSE =
1/2
⎧ ∑ (εiλ )2/N ⎫⎬ ⎨ k=1 ⎩ ⎭
where N is the number of spectra measured for one training site and λ is the total number of bands. In this instance, the optimum spectrum would be the one that produces the lowest average root mean square error. Columns and rows store the model number, corresponding to one column and row for each spectrum segment
2.2.3. Extracting the peanut canopy end-members Pixels of each training site were mathematically evaluated to define the pixels with the most representative spectral signatures for the training sites. They were, by definition, end-members. The pure end-members that best represented each training site were determined by using the VIPER tool. VIPER is a computer software
Fig. 8. Vegetation Mask. 567
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Fig. 9. Pixels Occupied Mostly by Peanut Canopy.
that, at this band, mature peanuts have more absorption than younger ones. This is due to an increase in chlorophyll a to chlorophyll b ratio. The chlorophyll a to chlorophyll b ratio increased with increasing peanut maturity (Banks and Eskins, 1981). The same phenomenon occurs at band 8 (690–700 nm), but to a lesser extent.
2.3. Building peanut leaf spectra-peanut maturity level mathematical PLS model Several mathematical models were tested to calibrate the VNIR-NIRSpectral Reflectance of peanut maturity level end-members against the peanut maturity expressed as the number of days after planting. Partial least squares (PLS) is a method for constructing predictive models when there are many highly collinear factors. Partial least squares regression is an extension of the multiple linear regression model. In its simplest form, a linear model specifies the relationship between a dependent (response) variable Y, and a set of predictor variables, the X's:
Therefore, bands 6 and 8 are good indicators for peanut maturity. The modification in leaf reflectance can be analyzed by using these two bands, but results will be more noticeable when information is combined by calculating band ratios. Therefore, end-member spectra were used to calculate the following vegetation ratios based on bands 6 and 8. These ratios were used to build the PLS model instead of using the individual bands:
Y = c 0 + c1X1 + c 2X2 + ...+c p Xp Where,
– The Normalized Difference Vegetation Index (NDVI): two NDVI ratios were calculated, one use Rikola Red band 6 and the other used the Red band 8, in addition to Rikola NIR band 12. – Normalized Difference Greenness Index (NDGI): this ratio was firstly used by Chamard et al. (1991). Two NDGI ratios were calculated one use Rikola Red band 6 and the other used the Red band 8, in addition to Rikola Green band 2.
c0 is the regression coefficient for the intercept ci are the regression coefficients (for variables 1 through p) computed from the data The partial least squares model of Origin software was populated with the values of the four vegetation indices as a set of predictor variables and the corresponding values of Days After Planting (DAP) as dependent variables. 3. Results and discussion By comparing the end-member spectral signatures of different maturity stages as shown in Fig. 11. It was found that: – At near-infrared (NIR) ranges, no significant differences existed between the peanuts spectral signatures at regardless of maturity stage – At visible (VIS) ranges, the blue, green and red bands were primarily associated with the total chlorophyll content (chlorophyll a and b), but, total chlorophyll content became constant at a specific stage of maturity (Rowland et al., 2008). This explains the similarity of the spectra of peanuts of different maturity levels in the green band – There is a high absorption peak at the red band 6 (655–665 nm) due to the high absorption of chlorophyll a at this band. It was noticed
NDVI 1 =
b12 − b6 b12 + b6
NDVI 2 =
b12 − b8 b12 + b8
NDGI 1 =
b2 − b6 b2 + b6
NDGI 2 =
b 2 − b8 b 2 + b8
Where, b2 is the green band centered at 545 nm b6 is the red band centered at 660 nm b8 is the Red band centered at 695 nm b12 is the NIR band centered at 785 nm
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Fig. 10. Training sites (in red) for the 6 July 2016 imagery.
c2 = −36.31639 c3 = −90.93535 c4 = −95.04471
The ratios were calculated for the spectra of all the training sites collected for various maturity stages as shown in Table 2 and used as input for the PLS regression module. Ratios were used as PLS independent variables (predicators) and maturity stages (expressed with the number of days after planting) were used as the dependent variable. The partial least square prediction model is expressed with the following equation:
Diagnostics plots are residual plots of Y and X as shown in Fig. 12. They were used to judge the quality of the model. Overall, it can be said that the peanuts leaf cover spectra-peanut maturity level mathematical PLS model is reasonable because:
DaysafterPeanutPlanting = c0 + (c1 ∗ NDGI1) + (c2 ∗ NDVI1)
– The predicted values-actual values graph indicates that the model fit well the first component – In the predicted values-residual graph, residuals are randomly distributed around zero indicating that there is no drift – The probability plot of residual shows that the result falls in a line, which means the variance is normally distributed
+ (c3 ∗ NDGI2) + (c4 ∗ NDVI2) Where, c0 = 250.22419 c1 = −85.90345 569
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Fig. 11. Comparison between End-member Spectral Signatures at Different Maturity Stages (DAP = 70 in red and DAP = 90 in black).
The model was constructed with data taken in 2017. Comparison between the model- predicted and real values, presented in Table 4, shows that the model does not give an accurate estimation in the first two months since planting, but accuracy of the model increases with time. This may due to the fact that the difference between chlorophyll a and b become more significant in mature peanuts more than immature plants. The overall assessment of the model indicates that the model needs to be calibrated for more precise prediction of the peanut maturity. This could be achieved by expanding the peanut maturity, versus peanut leaf spectra, database by taking data more frequently, especially close to harvest. Data collection should be started two months after planting when the ratios between chlorophyll a and b become more detectable in the leaf reflectance spectra.
Table 2 Data used as input to the PLS regression model. Color
Band Number
Spectral Range(nm)
Green
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
500–510 540–550 570–580 600–610 620–630 655–665 680–690 690–700 700–710 710–720 720–730 780–790 830–840 860–870 885–895
Red
Red Edge
Near Infrared
4. Conclusions Comparison between the model- predicted and real values showed that the model does not give an accurate estimate of maturity up to 60 days after planting, but the accuracy of the model increases with time. This may due to the fact that the difference between chlorophyll a and b become more significant in mature peanuts more than immature ones. The overall assessment of the model indicates that the model needs to be calibrated for more precise prediction of the peanut maturity. This could be achieved by expanding the peanut maturity, versus peanut leaf spectra, database by taking data more frequently, especially close to harvest. Data collection should be started two months after planting when the ratios between chlorophyll a and b become more detectable in the leaf reflectance spectra. Once properly calibrated, this approach to predicting peanut
A summary, the significance of each one of the vegetation indices within the prediction model is given in the VIP plot. The plot demonstrates that RGBVI is the most significant compared to the other indices, while the TGI index is the least as shown in Fig. 13. The model presented here was built using data collected in 2016. In order to validate the model, multispectral camera imagery was used to take images of the same peanut field one year later. The images were taken using the same multispectral scanner and the same spectral bands used to build the PLS model. These images were taken 48, 60, 80 and 121 days after planting. The ratios 1, 2, 3, and 4 were calculated for the spectral signatures of training sites selected randomly in the peanut field in 2017 and are shown in Table 3. 570
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Fig. 12. PLS diagnostic plots. Table 3 Data used for model validation.
Fig. 13. VIP plot of the independent variables of the peanut leave reflectancewater stress prediction model.
maturity would be an extremely valuable tool for farmers. The approach is quantitative compared with the more subjective peanut board and much for efficient.
Training site
Days after plantation
Ratio 1
Ratio 2
Ratio 3
Ratio 4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
48 48 48 48 48 48 60 60 60 60 60 60 80 80 80 80 80 80 120 120 120 120 120 120
0.817 0.798 0.791 0.787 0.865 0.813 0.717 0.769 0.767 0.701 0.686 0.803 0.8315 0.807 0.806 0.8665 0.814 0.833 0.701 0.627 0.639 0.643 0.791 0.617
0.722 0.658 0.727 0.689 0.816 0.776 0.699 0.617 0.708 0.786 0.811 0.721 0.8165 0.708 0.766 0.7735 0.6885 0.7435 0.618 0.646 0.684 0.521 0.731 0.511
0.382 0.424 0.389 0.374 0.402 0.393 0.391 0.403 0.411 0.362 0.502 0.389 0.4065 0.444 0.4165 0.4925 0.473 0.4525 0.389 0.401 0.511 0.322 0.366 0.366
0.311 0.376 0.361 0.234 0.387 0.397 0.218 0.394 0.345 0.204 0.354 0.394 0.2495 0.3415 0.254 0.2655 0.2405 0.2305 0.205 0.201 0.172 0.122 0.129 0.171
Appendix A. Supplementary material Acknowledgement
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compag.2019.04.028.
We wish to acknowledge to support of the National Peanut Board for funding this research. 571
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Table 4 Comparison between predicted and actual maturity dates. Predict values
Real days after plantation
Error %
Training sites
Days after plantation
Average predicted days after plantation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
80.9894 80.10834 88.07043 96.75214 73.98829 77.49265 101.56597 88.55243 85.8856 97.84083 76.07689 83.98884 86.83941 84.27307 90.42956 77.3494 90.34679 88.39733 113.73493 115.83024 103.74882 137.40367 108.96785 131.31196
83
48
73%
89
60
48%
86
80
7.5%
118
121
2.4%
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