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Citation: Corrêdo, L.d.P.; Molin, J.P.;
Canal Filho, R. Is It Possible to Measure
the Quality of Sugarcane in Real-Time
during Harvesting Using Onboard NIR
Spectroscopy? AgriEngineering 2024,6,
64–80. https://doi.org/10.3390
/agriengineering6010005
Academic Editors: Changyuan Zhai,
Ning Wang and Jianfeng Zhou
Received: 1 November 2023
Revised: 15 December 2023
Accepted: 26 December 2023
Published: 9 January 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
AgriEngineering
Article
Is It Possible to Measure the Quality of Sugarcane in Real-Time
during Harvesting Using Onboard NIR Spectroscopy?
Lucas de Paula Corrêdo 1, * , JoséPaulo Molin 2and Ricardo Canal Filho 2
1Department of Agronomy, Federal University of Viçosa, Viçosa 36570-900, Brazil
2Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture (ESALQ),
University of São Paulo, Piracicaba 13418-900, Brazil; jpmolin@usp.br (J.P.M.); ricardocanal@usp.br (R.C.F.)
*Correspondence: lucas.corredo@ufv.br
Abstract: In-field quality prediction in agricultural products is mainly based on near-infrared spec-
troscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-
controlled conditions. This study proposed a framework for NIR spectroscopy sensing to measure
sugarcane quality during a real harvest operation. A platform was built to support the system
composed of the NIR sensor and external lighting on the elevator of a sugarcane harvester. Real-time
data were acquired in commercial fields. Georeferenced samples were collected for calibration,
validation, and adjustment of the multivariate models by partial least squares (PLS) regression.
In addition, subsamples of defibrated cane were NIR-acquired for the development of calibration
transfer models by piecewise direct standardization (PDS). The method allowed the adjustment of the
spectra collected in real time to predict the quality properties of soluble solids content (Brix), apparent
sucrose in juice (Pol), fiber, cane Pol, and total recoverable sugar (TRS). The results of the relative
mean square error of prediction (RRMSEP) were from 1.80 to 2.14%, and the ratio of interquartile
performance (RPIQ) was from 1.79 to 2.46. The PLS-PDS models were applied to data acquired
in real-time, allowing estimation of quality properties and identification of the existence of spatial
variability in quality. The results showed that it is possible to monitor the spatial variability of quality
properties in sugarcane in the field. Future studies with a broader range of quality attribute values
and the evaluation of different configurations for sensing devices, calibration methods, and data
processing are needed. The findings of this research will enable a valuable spatial information layer
for the sugarcane industry, whether for agronomic decision-making, industrial operational planning,
or financial management between sugar mills and suppliers.
Keywords: NIR sensor; technological quality; precision agriculture; spatial variability;
on-the-go sensing
1. Introduction
Proximal sensing is key for high-density data acquisition to analyze in-field spatial
variation and enable precision agriculture (PA) management. Yield data stands out as the
final expression of crop development variability [
1
]. However, many crops have their added
value tied to quality parameters [
2
], such as sugarcane, where the soluble solids content
(Brix), apparent sucrose (Pol), total recoverable sugar (TRS), and insoluble solids (fiber) are
monitored for differentiated payment of suppliers and to optimize industrial production.
Integrating both yield and quality data could provide valuable in-field information for
decision-making [
3
]. However, current technologies lack the capability for on-the-go
measurement of sugarcane quality [4].
While high-resolution monitoring of sugarcane yield is a recent possibility of increas-
ing adoption [
5
], sugarcane quality is still monitored at the laboratory by conventional
methods [
6
], sampling cargo from harvesting trucks in the sugarmill entrance. For feasible
AgriEngineering 2024,6, 64–80. https://doi.org/10.3390/agriengineering6010005 https://www.mdpi.com/journal/agriengineering
AgriEngineering 2024,665
high-density monitoring, real-time and practical solutions are essential, as conventional
methods are costly, time-consuming, and environmentally unfriendly.
Near-infrared spectroscopy (NIR) has emerged as a new technology for the analysis
of agricultural products [
2
]. The interaction between electromagnetic energy and matter
produces the vibration of molecular bonds containing relatively heavy atoms (C, N, O, and
S) attached to a hydrogen atom, associated with water and organic compounds [7,8]. NIR
equipment used on static and standardized samples (off-line analysis), as for monitoring
Brix and Pol in the sugar mill laboratory [9], has been miniaturized for in-line monitoring
of moving samples, finding diverse commercial applications such as in the fertilizer in-
dustry [
10
] and fruit quality monitoring [
11
–
13
]. In agriculture, in-line measurements are
mainly observed in the scientific community, embedding sensors in the machinery for soil
attribute characterization [
14
–
16
] and for grain and forage quality measurements on the
harvesters [17–19].
Embedded sensors in agriculture are subject to dirt, moisture, vibrations, irregularity
in the sensor–sample distance, and lighting variation. There are proposed methods to
overcome environmental effects and potentialize prediction accuracy, such as advanced
filtering methods by orthogonalization [
20
], machine learning [
21
,
22
], and calibration
transfer models [
23
,
24
]. The calibration transfer method piecewise direct standardization
(PDS) showed substantial improvements for on-board prediction of soil organic carbon
using NIR spectroscopy [24].
Embedding a NIR sensor on the harvester for sugarcane quality measurement showed
its first promising results when testing different static sensing approaches for billet sam-
ples [
25
,
26
]. Sampler models for measurements at the elevator of the harvester were
proposed [
27
], and Phetpan et al. [
28
] designed a simulated harvester elevator for con-
trolled studies, employing a measurement chamber with an optical fiber spectrometer and
external lamps. Their analysis achieved promising results, including 0.30% Brix measure-
ment accuracy and a coefficient of determination (R
2
) of 0.79. Udompetaikul et al. [
29
]
investigated the unevenness of cane levels in the elevator under controlled conditions to
calibrate prediction models. They developed a combined model that addressed variations,
achieving a root mean squared error of prediction (RMSEP) of 0.42% for both full and half
delivery levels in two outer sets.
In-field sugarcane quality data enable sustainable site-specific management [
1
]. How-
ever, the technique must extend beyond controlled conditions [
3
]. This study hypothesized
that enough knowledge was built to embed a system of NIR spectroscopy sensing in a
sugarcane harvester during real operation. The goal was to develop an adequate instru-
mentation for data acquisition, then implement advanced data processing techniques with
calibration transfer models for predicting Brix, Pol, Pol of cane, TRS, and fiber.
2. Materials and Methods
2.1. NIR System Framework in Sugarcane Harvester
The microNIR 1700 spectrometer was used (Viavi Solutions, JDSU Corporation, Milpi-
tas, CA, USA), which contains two tungsten light bulbs as a radiation source for measuring
samples close to its window [
7
], equipped with a Linear Variable Filter (LVF) and an InGaAs
detector array of 128 pixels. It operates in the spectral range from 908 to 1676 nm, with
a spectral resolution of 6.19 nm, resulting in 125 wavelengths. Adaptations were made
to embed the sensor into the sugarcane harvester. The microNIR was linked to a HUB
module, converting the signal from USB to Ethernet for signal stabilization [
13
] (Figure 1a).
A voltage regulator module decreased the harvester’s battery voltage from 12 to 5 V. This
setup was enclosed in cast iron and featured a sapphire window (Figure 1b).
AgriEngineering 2024,666
Figure 1. MicroNIR spectrometer (a); device bottom view (b); instrumentation of sensors and
radiation sources on the measurement platform on-board of the harvester elevator (c); detail of the
measurement chamber position and GNSS receiver in the sugarcane harvester (d).
The sensor was embedded on the top of the harvester elevator, immediately before
the secondary extractor, in which the billets are partially cleaned of coarser impurities [
27
].
A platform was built to set 200 mm between the sapphire window and the conveyor belt
bottom and 40 mm over the conveyor slats (Figure 1c). Rubber vibration dampers were
used to fix the setup on the elevator. Four halogen lamps (AR111 type, 12 V and 50 W each)
were positioned as electromagnetic radiation sources [
28
], at the top corners of the setup,
420 mm away and at a 45
◦
angle, aiming towards the center below the sapphire window.
This entire system was covered in a galvanized steel box (800
×
800
×
600 mm), with the
inside painted matte black and the outside painted white, to minimize interference from
external lighting.
The sensor signal is logged as spectral absorbance values, received by a dedicated
NIR software (Spectral Soft—Spectral Solutions, São Paulo, SP, Brazil) on a laptop in
the harvester cab, and combined with the positional signal from a Global Navigation
Satellite System (GNSS) receiver (SMART6-L™, Novatel Inc., Calgary, AB, Canada) using
TerraStar-C correction with the geo-satellite AORW connection (NovAtel Inc., Calgary, AB,
Canada), providing an accuracy of
±
0.09 m (Figure 1d). The GNSS was positioned above
the harvester cab, in line with the base cutter.
2.2. Field Experiment and Data Acquisition: On-the-Go Measurements
The experiment was performed during three consecutive days of 2020/2021 harvest
season in a commercial second ratoon sugarcane field cultivated with CTC 4, with
1.50 m
between rows. Although the study area is contiguous, we partitioned the collections
into three distinct zones, labeled FA (1.72 ha), FB (3.32 ha), and FC (2.02 ha), respectively
(Figure 2).
AgriEngineering 2024,667
Figure 2. Location of experimental fields (Fields A, B, and C, respectively FA, FB, and FC), on-board
measurement points during harvest, and sampling points for sugarcane and soil. The relief conditions
are shown in detail.
The NIR software allows on-the-go measurements that involve a calibration step
followed by continuous data acquisition. Calibration was performed daily before data
acquisition and at noon. The distance of sugarcane billets to the sapphire window decreases
the spectrum signal-to-noise ratio due to reduced light intensity. Thus, optimizing spec-
trometer integration time is essential to mitigate the loss in photometric intensity. A white
reference (100% reflectance) was obtained by positioning a 0.40 m
2
white barium sulfate
plate (BaSO
4
) beneath the sapphire window in the measurement chamber using halogen
lamps [
30
]. A number of 50 scans and an integration time of 35 ms (0.7 ms per scan), which
is equivalent to 1.75 s for each spectral measurement, were set to obtain around 50,000 to
60,000 raw counts of photometric intensity, as recommended by the manufacturer. A dark
reference was obtained in the same environment, without the plate and with the lights off.
The spectrometer acquires five to six spectra every 10 s. Anomalies are identified
through principal component analysis and Hotteling’s T
2
test at a 95% confidence level,
recording the average non-anomalous spectra. The harvester mean speed was 1 m s
−1
,
and the conveyor belt speed was 2 m s
−1
, common speed under real conditions [
28
,
29
].
Thus, it was necessary to perform an offset for measurement points that was based on the
constrained method [
31
], which uses the sensor batch time (10 s) and the feeding time of
the harvester, defined as the time between the entrance of sugarcane in the base cutter and
the spectra measurement at the elevator. The feeding time was visually assessed as 10 s,
agreeing with other studies and manufacturer information [
32
]. To determine the offset to
the centroid of the harvested line, the distance and time difference between consecutive
points were calculated, considering the total measurement time as the sum of feed and
batch time. The corrected coordinate was then derived by subtracting the horizontal offset
along the track from the initial position.
2.3. Reference Sugarcane Quality Analyses and Spectra Bench Acquisition
Sugarcane samples were randomly collected in the fields during on-board mea-
surements, respecting a minimum of two rows between each collection and performing
20 sugarcane
samples for fields FA and FB and 26 for FC. Periodically, the transshipment
trailer moved ahead of the harvester, dropping samples onto a canvas on the ground for
collection (Figure 3a). Collection points were recorded using a handheld GNSS receiver
(Garmin 62 s, accuracy of 5 m, Figure 3b) and further matched with the position of an
AgriEngineering 2024,668
on-board measured spectra to compose the datasets for data modeling. The billets were
packed in plastic bags and sent to the laboratory, where they were stored at 2
◦
C to minimize
the degradation of organic compounds. The next day, fiber, Brix, Pol, Pol of cane, and TRS
were assessed following CONSECANA official procedures [6].
Figure 3. Sample collection procedure for modeling and validation. Sugarcane billet samples being
dumped on a canvas on the ground (a); georeferencing sample collection (b).
Simultaneously with laboratory analysis, bench spectra were obtained for all 66 field sam-
ples using the identical NIR sensor employed for on-board measurements. At the laboratory-
controlled temperature (20
±
5
◦
C), sub-samples of defibrated cane were positioned on a plate
to standardize the sample surface. Spectral measurements were made in triplicate at three
random points on the surface, storing the average spectrum for each sample.
2.4. Multivariate Data Modeling for Sugarcane Quality Estimation
Two datasets were created: one for field spectra and another for bench spectra, each
containing the spectra of 66 samples with their corresponding lab analyses. Descriptive
statistics were applied to the sugarcane quality values determined conventionally. The
data was partitioned using the Kennard-Stone (KS) algorithm [
33
] for FA, FB, and FC,
comprising 70% for calibration (47 samples, 14 from FA and FB, and 19 from FC), and
30% for validation (19 samples, 6 from FA and FB, and 7 from FC). The KS algorithm was
applied to provide 70% of the data from each field designated for calibration, aiming to
generate a global calibration model for both fields without bias towards either. The KS
algorithm is frequently employed in data modeling to achieve a partition of calibration and
validation datasets with equivalent data distribution, which is desirable to test machine
learning models.
In the spectral preprocessing step, we applied the standard normal variate (SNV) [
34
]
to all spectra, followed by the second derivative of the Savitzky-Golay algorithm (second
SG) [
35
,
36
]. SNV was employed to mitigate deviations caused by particle size and scattering,
achieving this by centering each spectrum on its mean and scaling it by its standard
deviation. To further enhance the signal-to-noise ratio and minimize potential hurdles in
the data, the second SG was implemented with a specific window size of nine wavelengths.
This window size was determined through a careful selection process, guided by the
objective of optimizing the preprocessing procedure. Specifically, the choice of the nine-
wavelength window was based on its performance in achieving a lower calibration Root
Mean Square Error (RMSE) value, ensuring that the preprocessing parameters were tuned
to enhance the overall robustness and accuracy of the subsequent data analysis. The
PDS [
37
] was developed to apply the calibration transfer method in field conditions. This
multivariate method aims to extend the accuracy of a master model to new environmental
conditions or different instruments [
38
]. PDS relates wavelengths from a master spectrum
(e.g., defibrated sugarcane) to those of a secondary spectrum (e.g., field spectra). Widely
used, it effectively calibrates soil spectra from on-board sensing, eliminating noise and
humidity effects [
15
,
20
,
23
]. Its main advantages include using a small transfer set and noise-
AgriEngineering 2024,669
filtering due to its multivariate nature [
15
]. PDS remains a reference in studies assessing
new model transfer methods due to its consistent performance [7].
The bench spectra dataset served as the master spectra, with the field spectra dataset as
the secondary spectra for PDS modeling. The algorithm generated a transformation matrix
from the master and secondary spectra matrices, allowing calibration model transfer to
spectra measured under different conditions. For a complete explanation of PDS modeling,
see Wang et al. and Workman [
37
,
39
]. The transformation matrix, built with a predefined
window (w = 9) using the calibration dataset [
23
], was applied to the external validation
dataset, validating the models. This calibrated model was then extended to all on-board
spectra measurements. Principal component analysis (PCA) was conducted for exploratory
analysis before and after the calibration transfer application.
The prediction models were developed using partial least squares (PLS) regression
and leave-one-out cross-validation [
40
], as developed by Corrêdo et al. [
41
]. The PLS
regression is often adopted for spectroscopy studies since it can cope with multivariate
data, converting it into a new multi-dimensional coordinate system (loadings) through the
creation of a smaller number of orthogonal variables (latent variables—LV) [
42
,
43
]. The
optimal PLS models were determined based on the lowest number of latent variables (LV)
and a lower RMSE of the cross-validation value. Both algorithms (PCA and PLS) were
based on the NIPALS (Nonlinear Iterative Partial Least Squares) method.
Outliers in the calibration step were identified using Hotelling’s T2 for high leverage
and Q statistics for unmodeled residuals. Samples exceeding the 95% significance level for
both tests were removed from the spectral dataset. Reference values were assessed by the
RMSE of calibration (RMSEC), and samples with prediction errors exceeding
±
3
×
RMSEC
were considered outliers and excluded from the dataset, following the guidelines of ASTM
E1655-17 [
44
]. All modeling was conducted using MATLAB (MATLAB R2015a, The MathWorks
Inc., Natick, USA) and PLS-Toolbox 8.8 (Eigenvector Research Inc., Manson, WA, USA). The
models were evaluated in terms of their R
2
, relative root mean square error (RRMSE) in
calibration, cross-validation, and external validation, and by the ratio of performance to
the interquartile range (RPIQ, Equation (1)).
RPIQ =(Q3−Q1)
RMSE (1)
where Q
3
and Q
1
are the upper and lower quartiles, respectively. The RPIQ is often used
for the evaluation of prediction models based on spectroscopy data because it can better
represent the accuracy of predictions in relation to the spread of the population. As pointed
out in [
45
], as RPIQ is not based on standard deviation, it can be a more appropriate metric
for measurements without a normal distribution.
The best PDS-PLS models were then applied for the prediction of all on-board spectra
acquired. No established method exists for filtering online measurements in spectral
prediction models. Outliers in on-board data predicted by PDS-PLS were identified using
the average distance to k-nearest neighbors (KNN) in score space. Samples with KNN
values exceeding three were considered outliers and removed from the dataset.
2.5. Spatial Variability and Site-Specific Assessment of the Relationships among Sugarcane Quality
and Soil Attributes
The spatial prediction used the values predicted by the PDS-PLS models to be interpo-
lated by ordinary kriging (OK). OK requires the adjustment of a semivariogram model for
each attribute to be interpolated, obtained through Equation (2):
γ(h)=1
2N(h)∑N(h)
α=1{Z(xα+h)−z(xα)}2 (2)
where,
γ
(h) is the semivariogram for a distance vector (lag) hamong the observations z(x
α
)
and Z(x
α
+h). N(h) was the number of pairs separated by h. Spherical, Exponential, and
Gaussian theoretical models were tested for semivariogram adjustment. The model with
AgriEngineering 2024,670
the lowest cross-validation RMSE was selected [46]. Data were subsequently interpolated
into a 1-m grid using global point kriging. Geostatistical analysis and mapping were
conducted in QGIS 3.10.8 (QGIS Development Team, 2018).
3. Results and Discussion
3.1. Sugarcane Quality Properties Characterization
The implementation of the KS algorithm allowed for the creation of a calibration
set whose range mirrored that of the complete dataset (Table 1). In the complete dataset,
variations of approximately 4% for Brix, 4.9% for Pol, 3.2% for fiber, 4.3% for Pol of cane,
and 40 kg Mg
−1
for TRS were observed. The coefficients of variation (CV) for all sugarcane
quality attributes exhibited a range of 3 to 5%, both in the calibration by cross-validation
and prediction datasets. The interquartile range revealed discrepancies below 1% for Brix,
Pol, fiber, and Pol of cane, while TRS manifested variations between 5.62 and 9.76 kg Mg
−1
for cross-validation and prediction datasets.
Table 1. Descriptive statistics of the sugarcane quality properties for all collected samples (all data)
for the cross-validation (47 samples) and prediction data set (19 samples).
Property Data Set Min. Max. Mean S.D. C.V.
(%) Q1Q3Kurt. Skew.
Brix (%)
All data 20.10 24.05 22.09 0.73 3.30 21.63 22.55 3.43 −0.06
Cross-val. 20.10 24.05 22.14 0.79 3.57 21.63 22.58 3.34 −0.13
Pred. 20.92 23.03 21.98 0.57 2.59 21.51 22.37 2.24 −0.09
Pol (%)
All data 17.68 22.55 19.92 0.85 4.27 19.50 20.35 4.15 0.20
Cross-val. 17.68 22.55 19.93 0.93 4.67 19.50 20.39 3.99 0.21
Pred. 18.57 20.94 19.88 0.65 3.27 19.40 20.30 2.30 −0.07
Fiber (%)
All data 10.43 13.66 11.77 0.61 5.18 11.42 12.04 3.54 0.22
Cross-val. 10.43 13.66 11.77 0.62 5.27 11.50 11.99 3.92 0.46
Pred. 10.44 12.67 11.78 0.60 5.09 11.37 12.25 2.50 −0.45
Pol of cane (%)
All data 14.86 19.16 16.93 0.74 4.37 16.56 17.30 4.41 0.00
Cross-val. 14.86 19.16 16.95 0.79 4.66 16.57 17.19 4.42 0.03
Pred. 15.73 17.87 16.90 0.59 3.49 16.37 17.36 2.05 −0.29
All data 147.84 187.82 167.37 6.82 4.07 163.98 170.77 4.41 −0.05
TRS (kg Mg−1)Cross-val. 147.84 187.82 167.53 7.32 4.37 164.05 169.67 4.45 −0.03
Pred. 156.13 176.15 166.98 5.55 3.32 161.25 171.01 2.02 −0.28
Abbreviations: Minimum (Min.); Maximum (Max.); Standard Deviation (S.D.); Coefficient of Variation (C.V.);
lower quartile (Q
1
); upper quartile (Q
3
); Kurtosis (Kurt.); Skewness (Skew.); Cross-val. (cross-validation); Pred.
(prediction or external validation); TRS (total recoverable sugars).
Positive skewness was evident in Pol and fiber, whereas Brix, Pol of cane, and TRS
approached zero skewness. Positive kurtosis across all variables signified a concentration
of values around the mean and median, with distribution curves showing no flattening.
Most sugarcane quality attributes demonstrated significant (p< 0.05) Pearson’s correlations
surpassing 0.90, except for fiber, which exhibited non-significant correlations (Figure A1
in the Appendix A). This is expected since all this quality variables are related to sucrose
content, and other soluble solids, such as reducing sugars and non-sugars, account for less
than 2% of the total physicochemical constitution of sugarcane [41,47].
Characterizing the three fields evaluated, Field C (FC, Figure 4) showed lower ab-
solute mean values for all sugarcane quality properties than fields A and B (FA and FB,
respectively). On the other hand, these values were higher for field B (FB) than the other
fields. The smallest interquartile distance was observed for field B, especially for Pol of
cane and TRS.
AgriEngineering 2024,671
Figure 4. Boxplots of laboratory results for each quality property for all 66 samples collected from the
three fields (All), and for the individual fields A, B, and C (FA, FB, and FC).
3.2. Processing of Spectral Data
The first LV for each of the five considered sugarcane quality attributes accounted for a
great part of the variance within each population—99.30% for fiber, 96.67% for Brix, 94.43%
for Pol, 87.82% for TRS, and 87.49% for Pol of cane. Despite variations in the explained
variance percentages, all variables exhibited similar plots for the first loading (Figure 5a).
The smallest variation, primarily attributed to saccharides and the third overtone of O-H,
occurred between 900 and 1000 nm and is typically associated with cellulosic fiber [
48
].
Around 960 nm, a corresponding effect of the third overtone of C-H stretching, possibly
related to polysaccharides such as sucrose, was observed [
49
]. An intermediate variation
is evident between 1100 and 1200 nm, where the second vibrational frequency overtones
associated with C-H stretching and sugars were identified [
49
]. Additionally, spectral
bands around 1170–1180 nm, linked to the third overtone of C-H and unsaturated C=C
double bonds, typically associated with fiber, such as lignin, were noted [
48
]. The most
significant variation within the wavelength range of the equipment used in this study was
between 1300 and 1450 nm, stabilizing close to zero explained variance by the first latent
variable after this interval. Effects possibly related to C-H combinations and the O-H first
overtone were observed at 1360 nm [
50
]. Bands associated with sugars, including C-H and
O-H related bands, were identified around 1450 nm, represented by the second overtone of
O-H and polysaccharides linked to O-H [48].
The spectra acquired through on-board sensing showed substantial differences from
those obtained under bench conditions (Figure 5b), as expected [
3
,
27
]. For both defibrated
and on-board datasets, spectra preprocessing and the PDS method allowed substantial
mitigation of these differences (Figure 5c). Although the PDS allowed the spectra obtained
by on-board sensing to exhibit very similar morphology to the spectra obtained on the
bench, it still maintains slight differences, primarily in the bands corresponding to the
absorbance peaks with the highest data variance (Figure 5d).
AgriEngineering 2024,672
Figure 5. Mean and standard deviation of spectral data collected in real-time on the harvester
(a). First Partial Least Square loading sugarcane quality properties using near-infrared reflectance
spectroscopy (b). Spectra difference pre- and post-calibration transfer application (c). Detail of
post-transfer difference between spectra data (d). Differences among spectra of sugarcane billet
samples with on-board readings by the NIR sensor during the harvest and after processing in the
form of defibrated cane with bench readings before and after calibration transfer by Picewise Direct
Standardization (PDS) (e).
The PCA of the spectra of the measurements taken on board before and after the
calibration transfer confirms a certain robustness of the PDS applied to the spectral data,
which substantially reduced the variance due to measurement differences between the
bench and on-board data sets (Figure 6). The scores of the laboratory spectra are divided
into two clear groups (Figure 6a), in which a variance of 93.56% is explained by the first
two components. After applying the PDS model, both spectra are represented in the space
of the main data set, with the confidence interval of the field data contained in that of the
data collected on the bench (Figure 6b). The first two components explained 85.05% of the
variance in the data after the PDS calibration transfer. The variation in data obtained from
on-board measurements may be due mainly to adverse environmental factors intrinsic
to mechanized harvesting (e.g., variation in external brightness, presence of impurities
and dirt, vibrations, and surface irregularities of the measured samples). The calibration
transfer method for noise correction of spectral data collection on board the harvester was
reasonably successful, despite not being able to fully correct for environmental effects
related to harvesting.
AgriEngineering 2024,673
Figure 6. The principal component scores for on-board field prediction and defibrated cane sam-
ples measured in the laboratory before (a) and after (b) calibration transfer by Piecewise Direct
Standardization (PDS). The percentage of variance explained by each component is shown on the
axes in parentheses.
3.3. Sugarcane Quality Properties Prediction Models
All PLS prediction models used seven LVs, except fiber ’s, which used only four. Fiber
also presented the lowest R
2
and highest RRMSE values, with R
2
of 0.18 and RRMSE%
of 3.97 for on-board measurements and R
2
of 0.13 and RRMSE% of 4.16 for laboratory
measurements. The best performance was observed for Brix prediction (Figure 7). The
comparison between the prediction using laboratory-measured and on-board spectra
demonstrates the effectiveness of the PDS for on-board spectra acquisition during sugarcane
harvest. The RRMSE for Pol showed consistent values in both laboratory and on-board
measurements (2.51%). Pol of cane was closely matched, at 2.75% in the lab and 2.73%
on-board. Similar trends were observed for Brix and TRS, with values of 1.86% and 2.75%,
and 1.59% and 2.56%, for on-board and laboratory measurements, respectively. RPIQ
values were also comparable. The Pol and Pol of the cane were comparable for on-board
(2.10 and 2.14) and laboratory measurements (1.81 and 2.13). Brix and TRS were slightly
higher in lab measurements (2.46 and 2.28) than on-board measurements (2.10 and 2.12).
Fiber predictive performance was marginally higher for online measurements (1.88) than
for laboratory measurements (1.79).
Comparing with other studies, Nawi et al. [
25
] achieved a Brix prediction RMSEP
of 1.51% (relative accuracy: 8.47%) using NIR absorbance spectra from the outer surface
under controlled conditions. In cross-section measurements of billet samples under control,
Nawi et al. [
26
] achieved RRMSEP values up to 8.13% (RMSEP = 1.45%) for Brix prediction.
Phuphaphud et al. [
51
] obtained fiber prediction results of up to 5.49% (RMSEP = 0.63%)
directly from the outer surface of sugarcane stalks’ peel. In a study on waxy material and
measurement position’s effect on Pol prediction, Maraphum et al. [
52
] achieved relative
accuracy results of up to 6.25% (RRMSEP = 1.20%). The prediction performance in this
study, possibly due to the calibration transfer procedure, was comparable to or higher than
previously reported results.
Before our study, NIR spectroscopy for on-board Brix sensing was only tested in
controlled conditions using sugarcane harvester elevator replicas. Udompetaikul et al. [
29
]
found that sugarcane delivery levels in the elevator influenced prediction performance.
Despite the best results in a full delivery elevator, they calibrated a model for uneven cane
levels, achieving R
2
of 0.56 and RRMSEP of 1.86% and 1.83%, respectively. Our study
replicated this in field conditions, showing a 1.86% relative accuracy for the same quality
attribute, consistent with controlled conditions.
3.4. On-Board Data Analysis and Spatial Variability
Geostatistical analysis showed that sugarcane quality parameters were spatially depen-
dent across the field (Table 2). However, the nugget variance (C
0
) represents a considerable
portion of the sill variance (C
0
+ C
1
), implying that portion is not spatially related. Ac-
cordingly, the space dependence index (SDI) proposed by Cambardella et al. [
53
] show
that Brix is moderately spatially dependent in the three fields. In FA, all sugarcane quality
AgriEngineering 2024,674
properties showed moderate spatial dependence, except fiber, which showed weak spatial
dependence. In FB, all quality properties except Brix were weakly spatially dependent. In
FC, besides Brix, Pol was moderately spatially dependent, and the other properties were
weakly spatially dependent. Rodrigues et al. [
54
] fitted variograms to predict Brix, Pol, and
fiber from physical-chemical soil parameters and leaf nitrogen as predictor variables. The
results corroborated the present study, showing weakly to moderately spatial dependence
of all quality parameters evaluated, except for fiber, with strongly spatial dependence on
the second harvest season.
Figure 7. Predicted values by PLS-PDS models (on-board measurements) versus measured values by
conventional laboratory methods (lab measurements) for sugarcane quality properties. TRS: total
recoverable sugars.
AgriEngineering 2024,675
Table 2. Semivariogram model parameters of sugarcane quality properties used for mapping the
spatial variability for the three experimental fields.
Field Property Model Fit C0C0+ C1A (m) C0/(C0+ C1)
FA
Brix (%)
Exponential
0.060 0.098 33.62 0.61
Pol (%) Spherical 0.091 0.223 71.53 0.41
Fiber (%) Gaussian 0.036 0.043 92.93 0.84
Pol of cane (%) Spherical 0.071 0.161 75.74 0.44
TRS (kg Mg−1)Spherical 6.250 13.019 73.78 0.48
FB
Brix (%)
Exponential
0.252 0.363 64.48 0.69
Pol (%) Spherical 0.349 0.444 62.98 0.79
Fiber (%) Spherical 0.108 0.117 264.23 0.92
Pol of cane (%) Spherical 0.293 0.360 66.12 0.81
TRS (kg Mg−1)Spherical 25.176 31.016 66.01 0.81
FC
Brix (%)
Exponential
0.381 0.517 38.76 0.74
Pol (%)
Exponential
0.500 0.629 58.34 0.79
Fiber (%) Spherical 0.136 0.212 118.73 0.64
Pol of cane (%) Spherical 0.396 0.508 93.10 0.78
TRS (kg Mg−1)Spherical 34.424 44.395 97.67 0.78
Header abbreviations: nugget variance (C
0
); spatial dependent variance (C
1
); range (A); nugget-to-sill
[C0/(C0+ C1)]; total recoverable sugars (TRS).
Despite inherent measurement errors tied to technique limitations in mechanized harvesting,
including variable sensor-target distance and environmental challenges, a discernible spatial
structure is evident in variograms even within the small areas tested (1.7 to 3.3 ha). The range
of sugarcane quality properties spanned approximately 33–264 m, with sugar concentration
parameters (Brix, Pol, Pol of cane, and TRS) having a maximum range of 98 m. Notably, there was
variability at short distances, particularly for Brix (range: 34 to 65 m), indicating diverse quality
content regions in the fields. This aligns with findings by Johnson and Richard [
55
], who reported
a spatial correlation for yield and sugarcane quality parameters in Louisiana (USA) with ranges
from 26 to 133 m. Catelan et al. [
56
] also identified strong spatial dependence in Brix, Pol, fiber,
TRS, and sugarcane yield in a 445 ha area with a low sample density collected manually.
Sugarcane quality maps revealed significant variability across the experimental fields
(Figure 8), with pronounced distinctions among the fields themselves. Notably,
Field C
exhibited
a larger area with lower concentrations of all quality parameters compared to Fields A and B,
as depicted in Figure 4. Laboratory measurements for calibration and validation of prediction
models affirm this trend, indicating lower quality parameter contents in Field C compared
to Fields A and B. The modeling effectively discriminates between high and low values of
sugarcane quality properties, showcasing this distinction even within small areas and over
substantial distances.
Brix, Pol, fiber, and Pol of cane had range variations of 7.14%, 8.91%, 7.63%, and 9.27%,
respectively. The range of TRS was 8.62%, representing 14 kg Mg
−1
. The combined use of
this parameter with yield data may enable real-time maps of sugar produced per area. It
constitutes valuable information not only for the agronomic management aiming at the quality
of the product supplied to the mills but also for the industrial operation planning and financial
management of the entire operation between sugar mills and suppliers.
Achieving comparable predictive performance to methods of controlled conditions in
NIR spectroscopy is challenging due to factors such as sample surface uniformity and external
interferences [
7
]. This study confirmed the potential of the PDS calibration transfer method to
overcome some intrinsic adversities of mechanized sugarcane harvesting. In real-world field
conditions, the control of cane levels in the elevator may make the implementation of an on-
board system unfeasible. Therefore, it is necessary to explore diverse methods of post-processing
spectra combined with the optimization of sensor system parameters (e.g., sensor-target dis-
tance and electromagnetic radiation intensity). The combination of NIR instrumentation and
PDS application was crucial to the technique’s performance in a real sugarcane harvest sys-
AgriEngineering 2024,676
tem. We integrated harvester elevator acquisition, previously tested only under controlled
conditions [28,29], with spectra from sugarcane billets.
Figure 8. Sugarcane quality spatial variability maps for the three experimental fields measured by
the on-board NIR sensor on the harvester and predicted by PLS regression models combined with
the piecewise direct standardization calibration transfer method.
Despite the additional challenge presented by the narrow range of quality parameter
values [
28
], as observed in the small nearby fields of this study, the technique allowed the
identification of variability within the field. It demonstrated a prediction error comparable
to that of other studies, suggesting its potential utility for industry control and agronomic
decision-making. Furthermore, the goals of spatialized estimates in precision agriculture are
both qualitative and quantitative. In a study like this, the technology is expected to enable
mapping regions with tendencies toward lower or higher quality for localized management.
The high-density information collection would compensate for sensor accuracy [
3
], as the data
undergoes geostatistical analysis and interpolation. In summary, the mere ability to capture
variations in quality attributes in the field represents an advancement in spatial management
using precision agriculture techniques.
Future studies should extend this methodology to evaluate extensive commercial fields
across multiple harvest seasons and at various moments within the season, facilitating clearer
identification of spatial patterns for sugarcane quality parameters. Yield data can be integrated
to analyze relationships between quantity and quality parameters. Acquiring such a compre-
hensive database will enable the development of more robust calibration models with greater
variability in quality properties, leveraging the technique on a commercial scale.
Different external illumination sets and sensing distances can be tested to minimize the
obstruction of the sapphire window by dirt. Exploring higher frequencies of data collection and
employing unsupervised data processing techniques can further optimize transfer-related pa-
rameters. Additionally, improving model results can be pursued by combining more advanced
variable combination and calibration transfer methods based on NIR data already collected at
high density by the industry’s quality laboratory throughout the harvest. The implementation of
this sensor in harvester fleets is dependent on acquisition costs, necessitating the development
of system simplifications. The selection of spectral variables, a well-established chemometrics
technique, can assist in this task.
AgriEngineering 2024,677
4. Conclusions
This study has shown that it is possible to overcome the partially adverse effects of on-
board sensing on sugarcane harvesters and provide spatial data on crop quality. Piecewise
direct standardization allowed transferring NIR calibration models developed by partial least
squares regression on the bench from defibrated cane samples to perform post-processing of
data obtained by the on-board sensor in the harvester. Furthermore, the on-board sensor and the
proposed method for prediction allowed the spatial variability of sugarcane quality attributes
to be characterized. The results were consistent with previous studies, which strengthens the
conclusion about the effectiveness of the proposed method for onboard proximal sensing of
sugarcane quality to map the spatial variability of technological quality parameters.
Author Contributions: Conceptualization, L.d.P.C. and J.P.M.; methodology, L.d.P.C. and J.P.M.; vali-
dation, L.d.P.C., R.C.F. and J.P.M.; formal analysis, L.d.P.C.; investigation, L.d.P.C.; resources, L.d.P.C.
and J.P.M.; writing—original draft preparation, L.d.P.C.; writing—review and editing, L.d.P.C., R.C.F.
and J.P.M.; visualization, L.d.P.C., R.C.F. and J.P.M.; supervision, J.P.M.; project administration,
L.d.P.C.; funding acquisition, L.d.P.C. and J.P.M. All authors have read and agreed to the published
version of the manuscript.
Funding: This study was financed by grant #2018/25008-8, São Paulo Research Foundation (FAPESP),
which provided a doctoral scholarship to the first author.
Data Availability Statement: Data will be made available on request.
Acknowledgments: The authors acknowledge the support of Iracema Sugar Mill (São Martinho
Group) and Spectral Solutions Comércio e Serviço Ltda.
Conflicts of Interest: The authors declare no conflicts of interest.
Appendix A
Figure A1. Pearson’s correlation and distribution of frequency for sugarcane quality properties.
* Significant correlation (p-value < 0.05);
ns
non-significant correlation (p-value > 0.05). Blue dots are
the samples, and the black line is the correlation trend line. TRS (total recoverable sugars).
AgriEngineering 2024,678
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