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 Agricultural Science; Vol. 7, No. 1; 2025 
ISSN 2690-5396   E-ISSN 2690-4799 

https://doi.org/10.30560/as.v7n1p84 

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Study On the Detection of Dry Matter in Silage Corn Feed Based on 
Near Infrared Spectroscopy 

Changfeng Shao1,2 & Wenjing Zhu1,2 
1 School of Agricultural Engineering, Jiangsu University, Zhenjiang, China 
2 High-tech Key Laboratory of Agricultural Equipment and Intelligence, Jiangsu Province, China 

Correspondence: Wenjing Zhu, School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, 
China. Tel: 86-153-5388-4867. E-mail:  

 

Received: March 7, 2025   Accepted: April 10, 2025   Online Published: April 12, 2025 

 

The research is financed by Key Laboratory of Modern Agricultural Equipment and Technology Project 
(MAET202322) and National Natural Science Foundation of China Youth Fund Project (61901194). 

 

Abstract 

This study explored the application of a portable near-infrared (NIR) spectrometer for analyzing silage corn feed 
quality, specifically focusing on developing a quantitative detection model for dry matter content. Spectral data 
were collected within the 855-1890 nm range using a portable NIR spectrometer, and the dataset was partitioned 
into calibration and prediction sets using the SPXY algorithm. An Extreme Learning Machine (ELM) model 
optimized by Particle Swarm Optimization (PSO) was employed for modeling. Five preprocessing methods were 
evaluated: Moving Average Filter (MAF), Savitzky-Golay Filter (SGF), Multiplicative Scatter Correction (MSC), 
Standard Normal Variate (SNV) transformation, and First Derivative (FD). To enhance model performance, 
feature wavelengths were selected using three methods: Bootstrap Soft Shrinkage (BOSS), Competitive Adaptive 
Reweighted Sampling (CARS), and Iterative Retained Information Variable (IRIV). The optimal model combining 
SNV preprocessing with BOSS feature selection achieved a prediction correlation coefficient (𝑅 ) of 0.8708 and 
Root Mean Square Error of Prediction (RMSEP) of 0.6802. These results demonstrate the potential of portable 
NIR spectroscopy for rapid dry matter content determination in silage corn feed. 

Keywords: near-infrared spectroscopy, silage corn, dry matter content, machine learning optimization 

1. Introduction 

As one of the most cultivated and productive crops in the world, maize is not only an important link in the human 
food supply, but also a core raw material for livestock rearing(García-Chávez et al., 2020). In aquaculture, maize 
silage plays a decisive role as a key feed resource to improve productivity and economic efficiency(Ribas et al., 
2021). Silage technology maintains and enhances the nutritional value of the feed by creating anaerobic 
fermentation conditions (Fuglestad et al., 2021), which maximizes the use of plant nutrients and lays the foundation 
for the sustainable progress of the farming industry (Yin et al., 2024). Straw dry matter content is a basic and 
critical indicator in the silage process. Ideal storage results depend on the rapid detection and adjustment of the 
dry matter ratio. However, conventional chemical analysis is time-consuming and labor-intensive, and a 
breakthrough is needed(Büning-Pfaue, 2003) . Therefore, a rapid and non-destructive way to monitor the dry 
matter indexes in silage maize stover not only improves the speed of inspection and reduces the running cost, but 
also provides an important basis for establishing an efficient analytical system and optimizing feed utilization. 

Several studies have been conducted on the application of NIRS technology in agriculture.(Wu et al., 2005) 
developed a NIRS calibration model to accurately predict the cellulose content of corn stover by partial least 
squares regression (PLS).(Aureli et al., 2017) developed a NIRS calibration method for predicting the 
concentration of total phosphorus, phytate-P, and protein in feeds based on 14 plant samples from around the 
world. calibration method.(Ma et al., 2019) developed a high-precision model for predicting moisture, crude 
protein and soluble sugar content of mulberry leaves using a handheld NIRS. (Zhang et al., 2019)constructed a 
high-precision prediction model for moisture content by spectral analysis of 208 corn stover silage samples. These 
studies provide an important theoretical foundation and technical reference for this research. However, although 
the feasibility of NIR detection in silage nutrient quality testing has been verified, there are fewer studies on the 



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index of dry matter of silage corn feed, especially in the pre-processing methods, wavelength selection methods 
and modeling algorithms have not yet been studied in depth. In addition, silage corn feed generally requires on-
site field testing, which has certain requirements for portability. 

The aim of this study is to use a portable near-infrared spectrometer to collect the spectral data of silage corn feed, 
and to establish a high-precision dry matter prediction model for silage corn feed by combining multiple 
preprocessing methods and wavelength selection algorithms. The feature wavelengths were screened by 
Competitive Adaptive Resampling Algorithm (CARS), Bootstrap Soft Shrinkage Algorithm (BOSS), and Iterative 
Retention of Information Variables Algorithm (IRIV), and the optimization algorithm was used to preferentially 
select the modeling parameters, and the optimal preprocessing method, wavelength selection strategy, and 
modeling parameters were ultimately determined in order to establish a dry matter content prediction model with 
high prediction accuracy. This not only helps to improve the efficiency and accuracy of feed quality testing, but 
also provides technical support for the sustainable development of the farming industry. 

2. Method 

2.1 Test Materials 

The silage corn feed samples used in this experiment were collected in December 2024 from the silage corn 
fermentation pond of a dairy farm located in Tai'an City, Shandong Province. A total of 270 samples were gathered. 
Immediately after collection, the samples were placed into vacuum bags and vacuum-sealed. They were then 
transported and stored under refrigerated conditions to ensure their preservation. 

2.2 Near Infrared Spectroscopy Data Acquisition 

The NIR spectral acquisition system comprises an NIR spectrometer, light source, acquisition lens, and computer. 
The system utilizes the NIR-NT compact fiber-optic near-infrared spectrometer, manufactured by INSION in 
Germany, with a nominal wavelength range of 900-1700 nm. However, due to the manufacturing process, the 
actual wavelength range is 855-1890 nm. Prior to testing, the system requires a 30-minute preheating process to 
eliminate baseline drift. Subsequently, the spectrometer's focal length must be adjusted to ensure optical accuracy. 
The system's exposure time was set to 20 ms for the experimental parameters. During data acquisition, samples 
should be evenly distributed in the container to ensure they are within the aperture's coverage area. Using near-
infrared spectral data acquisition software, spectral data within the 855-1890 nm wavelength range were collected 
in this study. 

2.3 Measurement of Dry Matter Content 

The dry matter content was measured following the standard method outlined in GB/T 6435-2014 "Determination 
of Moisture in Feed" using the oven-drying technique. The procedure is described as follows: 

A beaker was first dried at 103 °C for 30 minutes in a drying oven and cooled to room temperature in a desiccator. 
Its mass (m₁) was then recorded. Subsequently, 5 g of silage corn feed was weighed into the pre-dried beaker and 
dried at 103 °C for 4 hours. During the drying process, the sample was periodically monitored until a constant 
weight was achieved, defined as a difference of less than 0.1% between consecutive measurements. After drying, 
the beaker and dried sample were allowed to cool to room temperature in a desiccator, and the total mass (m₂) was 
recorded. The dry matter content was calculated using Equation (1). 

𝑋
𝑚 𝑚

𝑚
100% 1  

In the Equation, 𝑚 represents the initial weight of the beaker (g), 𝑚 is the weight of the sample (g), and 𝑚  refers 
to the weight of the beaker and the sample after drying (g). 

The statistical analysis results for the dry matter indices of the test samples are presented in Table 2. 

 

Table 1. Statistical table of dry matter content (%) of silage maize forage 

Quality Indicators Minimum Maximum Mean Standard Deviation 

DM 28.63 35.17 31.47 2.30 

 

 

 



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2.4 Spectral Data Preprocessing 

The acquisition of near-infrared spectral data is inevitably affected by various factors, including stray light, signal-
to-noise interference, diffuse light reflection, and spectral baseline shifts. To eliminate these interferences and 
establish a reliable prediction model, preprocessing of the spectral data is essential. In this study, five preprocessing 
methods were applied to the spectral data: moving average filtering (MAF), Savitzky-Golay filtering (SGF), 
multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, and first-order derivative 
(FD) processing. 

2.5 Characteristic Wavelength Selection 

The selection of wavelengths for near-infrared (NIR) spectroscopy is crucial for reducing model complexity, 
improving prediction accuracy, and enhancing scanning speed. By identifying wavelengths closely related to the 
target analyte, data dimensionality can be reduced, and the model structure can be optimized. This, in turn, 
minimizes the model size and enhances prediction performance. Additionally, selecting characteristic wavelengths 
helps eliminate noise and interference, thereby increasing the model's robustness. In this study, Competitive 
Adaptive Reweighted Sampling (CARS), Bootstrap Soft Shrinkage (BOSS), and Iterative Retention of Informative 
Variables (IRIV) methods were employed to select the key wavelengths. 

2.6 Spectral Modeling Methods 

Extreme Learning Machine (ELM) is a machine learning approach derived from the Single Hidden Layer 
Feedforward Neural Network (SLFN). It is effective in handling complex nonlinear fitting problems and is 
particularly suitable for large-scale training datasets (Wang et al., 2018) . Based on the principle of structural risk 
minimization, ELM enhances data fitting capabilities by randomly mapping the linear output function to achieve 
efficient nonlinear transformations in high-dimensional feature spaces. In this study, the particle swarm 
optimization (PSO) algorithm was employed to optimize the key parameters of the ELM model. 

Particle Swarm Optimization (PSO) is an evolutionary algorithm inspired by the collective intelligence of groups. 
It efficiently solves complex optimization problems by simulating the collaborative search mechanisms among 
individuals in a population(Huang et al., 2021) . The PSO algorithm utilizes particle position and velocity as core 
parameters, conducting searches through dynamic updates of the individual best and global best solutions, 
ultimately converging on the global optimal solution. Compared to traditional optimization algorithms, PSO is 
characterized by its simplicity, high efficiency, and ease of parallel implementation. It has been widely applied in 
function optimization and parameter tuning in machine learning. In this study, PSO was used to optimize the 
hyperparameters of the ELM model, including the number of neurons in the hidden layer and the weights, thereby 
significantly improving the model's generalization performance and prediction accuracy. 

2.7 Model Evaluation Metrics 

In model evaluation, the key metrics include the calibration set correlation coefficient (𝑅 ), the calibration set root 
mean square error (RMSEC), the prediction set correlation coefficient (𝑅 ), and the prediction set root mean square 
error (RMSEP). The closer the values of 𝑅  and 𝑅  are to 1, the stronger the model's explanatory and predictive 
abilities. Conversely, lower values of RMSEC and RMSEP indicate higher model precision. 

2.8 Data Processing Methods 

Spectral preprocessing and dataset partitioning were performed using NIRSA 5.3 software. Wavelength selection 
and model development were conducted in MATLAB R2022a. The IRIV algorithm for feature wavelength 
extraction was implemented using the libPLS 1.98 toolbox (Li et al., 2018). 

3. Results 

3.1 Dataset Division 

The dataset was divided using the SPXY algorithm. A total of 189 samples (70% of the dataset) were used as the 
calibration set, while 81 samples (30% of the dataset) were assigned to the test set. 

3.2 Spectral Preprocessing Analysis 

To analyze the effects of different near-infrared (NIR) spectral preprocessing methods on the prediction model for 
the dry matter content of silage corn feed, five preprocessing techniques—Savitzky-Golay filtering (SGF), moving 
average filtering (MAF), multiplicative scatter correction (MSC), standard normal variate (SNV), and first-order 
derivative (FD)—were applied to the raw spectral data. The original spectra and the preprocessed spectral data are 
shown in Figure 1. 



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As illustrated in Figure 1, after SGF and MAF preprocessing, the spectral curves became smoother, noise 
fluctuations were significantly reduced, and the curves were largely consistent with the original spectral profile. 
Following MSC and SNV preprocessing, the spectral curves demonstrated higher concentration, scattering effects 
were effectively reduced, and spectral features became more prominent. After FD preprocessing, small feature 
variations in the spectral data were revealed, characteristic peaks were significantly enhanced, and the signal-to-
noise ratio was improved to a certain extent. 

 

Figure 1. Original Spectra and Preprocessed Spectra 

 

Table 2 shows the performance of PSO-ELM prediction models developed using different preprocessing methods 
across the full wavelength spectrum. Models developed using raw spectral data yielded a prediction set correlation 
coefficient (𝑅 )and RMSEP values of 0.8228 and 0.7774, respectively, indicating the feasibility of direct modeling 
despite the potential interference from noise and scattering effects. 

Models based on MAF preprocessing exhibited slightly diminished prediction set performance (𝑅 = 0.8179, 
RMSEP = 0.7871). Although MAF effectively smooths spectral data and reduces high-frequency noise, it may 
simultaneously remove critical spectral information, resulting in reduced prediction accuracy. In contrast, SGF 
preprocessing improved prediction set performance (𝑅 = 0.8375, RMSEP = 0.7472), successfully mitigating 
overfitting tendencies in the original data and enhancing the model's generalization ability. 



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The MSC and SNV preprocessing techniques produced distinct outcomes, with SNV delivering superior prediction 
set performance (𝑅 = 0.8413, RMSEP = 0.7680), outperforming both MSC-processed and raw spectral data. The 
superior performance of SNV likely results from its standardized normalization process, which effectively reduces 
scattering-induced variability, minimizes bias, and improves the reliability of the model. 

First-order derivative (FD) preprocessing significantly improved calibration set performance but paradoxically 
reduced prediction set performance 𝑅 = 0.8132, RMSEP = 0.7687). This inverse relationship suggests that while 
FD enhances the resolution of spectral features, it also amplifies noise components and increases the risk of 
overfitting, leading to diminished predictive performance. 

 

Table 2. Modeling Performance Based on Full-Wavelength Spectral Data 

Preprocessing 
Modeling 
Methods 

Calibration Set Test Set 

𝑅  RMSEC 𝑅  RMSEP 

RAW 

PSO-ELM 

0.8817 0.7309 0.8228 0.7774 

MAF 0.8658 0.7752 0.8179 0.7871 

SGF 0.872 0.7585 0.8375 0.7472 

MSC 0.8754 0.7444 0.8369 0.778 

SNV 0.8723 0.7529 0.8413 0.768 

1D 0.8873 0.7529 0.8132 0.7687 

 

3.3 Characteristic Wavelength Extraction 

3.3.1 Competitive Adaptive Reweighted Sampling 

The Competitive Adaptive Reweighted Sampling (CARS) algorithm is a wavelength selection method based on 
Darwinian evolutionary theory(Li et al., 2009) , designed to optimize wavelength selection through iterative 
statistical analysis. This algorithm integrates adaptive reweighted sampling (ARS) with partial least squares (PLS) 
to identify wavelengths with larger absolute regression coefficients during each iteration. Through multiple 
iterations, the CARS algorithm searches for the subset of wavelengths that minimizes the root mean square error 
of cross-validation (RMSECV), thereby identifying the most representative set of optimal wavelengths. 

In this study, the optimal principal component of the PLS model is determined by Monte Carlo cross-validation 
(MCCV) and input into the competitive adaptive reweighted sampling (CARS) algorithm to realize the feature 
wavelength screening. The specific process(Liu et al., 2023)  is as follows: first, the spectral data are centered and 
preprocessed to eliminate baseline bias; based on the MCCV method, the model stability is assessed and the 
optimal principal component fraction is determined to be 15 by 50 independent random samples (80% of samples 
taken each time) combined with a five-fold cross-validation; subsequently, the principal component fraction is 
inputted into the CARS algorithm as a parameter to carry out the Wavelength screening: in 500 iterations of 
optimization, a subset of candidate variables is generated by Monte Carlo sampling, low regression coefficient 
variables are gradually eliminated using exponential decreasing function (EDF), and high weighted wavelengths 
are competitively retained in combination with adaptive reweighting sampling (ARS). Finally, the optimal 
wavelength subset with the smallest RMSECV is screened by 50 independent modeling validations using the 50-
fold cross-validation root mean square error (RMSECV) as a criterion. 

As shown in Figure 2, Figure 2-a depicts the relationship between the number of samples and the number of 
sampled variables, which overall exhibits a smooth decreasing trend. This indicates that a large number of 
irrelevant variables are gradually eliminated as the number of samples increases. Figure 2-b illustrates the changing 
relationship between the number of samples and the root mean square error of cross-validation (RMSECV). At the 
early stages, the RMSECV decreases rapidly, consistent with the trend observed in Figure 2-a. The RMSECV 
reaches its minimum value at 21 samples, after which it begins to increase. 

Figure 2-c shows the relationship between the regression coefficients of the wavelength variables and the number 
of samples. The blue dashed line marks the location of the minimum RMSECV value, which corresponds to Figure 
2-b, indicating the optimal subset of wavelengths. Following screening by the CARS algorithm, a total of 24 
characteristic wavelengths were selected, accounting for 18.8% of the overall wavelengths. The distribution of the 
characteristic wavelength variables obtained through the CARS algorithm is shown in Figure 3. 



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Figure 2. CARS screening results 

 

Figure 3. CARS-Selected Wavelength Distribution 

 

3.3.2 Bootstrap Optimized Subset Selection 

The BOSS algorithm (Bootstrap Optimized Subset Selection) is an iterative optimization method for feature 
wavelength screening. Its core concept is to combine bootstrap sampling and dynamic assignment strategies to 
identify the optimal sub-model by generating random feature subsets and employing Partial Least Squares (PLS) 
modeling with Root Mean Square Error of Cross-Validation (RMSECV) as the criterion (Deng et al., 2016). During 
the iterative process, regression coefficients are utilized to construct weight vectors, dynamically adjusting the 
probability of variable selection.  

This approach prioritizes the retention of highly contributory variables through weighted bootstrap selection, 
ultimately selecting the variable combination with the lowest RMSECV as the optimal solution while balancing 
data diversity and model stability. 

As a feature selection algorithm based on model cluster analysis, this section highlights the results of its practical 
application. Figure 4illustrates the relationship between the RMSECV and the number of iteration rounds during 
cross-validation using the BOSS algorithm. The results indicate that the RMSECV exhibits a dynamic trend of first 
decreasing and then increasing as the number of iterations progresses. At the 3rd iteration round, the RMSECV 
reaches its minimum value, marking the precise localization of the optimal variable combination. Further 
increasing the number of iteration rounds results in a rebound of the RMSECV. This rebound occurs because key 
information variables are unintentionally eliminated. The distribution of feature wavelengths selected by the BOSS 
algorithm across the entire wavelength range is shown in the accompanying figure, with the selected wavelengths 
accounting for 26.6% of the total wavelengths. The distribution of the characteristic wavelength variables obtained 
through the CARS algorithm is shown in Figure 5. 



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Figure 4. BOSS screening results 

 
Figure 5. BOSS-Selected Wavelength Distribution 

 

3.3.3 Iterative Retained Information Variable 

The Iterative Retained Information Variable (IRIV) method is a feature selection algorithm based on binary 
random matrix rearrangement filtering(Yun et al., 2014). This method classifies variables into four categories: 
strong information, weak information, no information, and interfering variables. Through multiple rounds of 
iterations, the algorithm gradually filters out the optimal subset of features, thereby reducing the impact of 
redundant and interfering variables on model performance. 

As shown in Figure 6, the IRIV algorithm exhibits a gradual reduction in the number of variables during the feature 
extraction process for the spectral data of silage corn feed. The process begins with an initial screening of global 
variables, followed by a reverse elimination mechanism to evaluate the contribution of each variable to the model's 
predictive performance. Through this stepwise elimination of low-contributing variables, 23 key feature 
wavelengths—accounting for approximately 18% of the original feature space—were ultimately retained. During 
the backward elimination process, the root mean square error of cross-validation (RMSECV) is used as the 
evaluation metric, and the model is re-modeled at each step of variable removal to ensure that overall model 
performance is not significantly degraded. The distribution of the characteristic wavelength variables obtained 
through the CARS algorithm is shown in Figure 7. 



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Figure 6. IRIV screening results 

 

Figure 7. IRIV-Selected Wavelength Distribution 

 

3.4 Modeling Based on Characteristic Wavelengths 

Table 3 summarizes the performance of prediction models built using different characteristic wavelengths. During 
the construction of the ELM model, the modeling parameters were optimized using the PSO algorithm. From the 
table, it can be observed that the CARS, BOSS, and IRIV algorithms effectively reduce the number of wavelengths 
required for modeling. Consequently, the input parameters for the prediction model are reduced, which enhances 
the real-time performance of online detection systems. 

The correlation coefficients (𝑅 ) of the model prediction sets based on the characteristic wavelengths all exceed 
0.85, demonstrating superior performance compared to models built using the original data. Among the tested 
models, the SNV-BOSS-PSO-ELM model demonstrated the best performance, achieving an 𝑅  of 0.8708 and an 
RMSEP of 0.6802. 

 

Table 3. Modeling Performance Based on Characteristic Wavelengths 

Modeling 
Methods 

Wavelength 
Selection Methods 

Number of 
Features 

Calibration Set Test Set 

𝑅  RMSEC 𝑅  RMSEP 

PSO-ELM 

CARS 24 0.8995 0.687 0.8664 0.6895 

BOSS 34 0.9142 0.6306 0.8708 0.6802 

IRIV 23 0.9027 0.666 0.867 0.6927 

 



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3.5 Experimental Summary 

In this experiment, a quantitative detection model for the dry matter content of silage corn feed using portable 
near-infrared spectroscopy was developed, with silage corn feed as the research object. The results indicate that 
spectral data preprocessed using the Standard Normal Variate (SNV) method outperformed the original data and 
data processed with other preprocessing methods. During the PSO-ELM modeling process, the model constructed 
using the characteristic wavelengths selected by the BOSS algorithm demonstrated the best prediction 
performance. 

By analyzing the fitted scatter plots (Figures 8–10) of the prediction results from the three models based on 
different wavelength selection algorithms for the dry matter content of silage corn feed, it is evident that the SNV-
BOSS-PSO-ELM model exhibited a better fitting effect compared to the other models. Its prediction results are 
more concentrated, which aligns with the data presented in the table. These results confirm that the SNV-BOSS-
PSO-ELM model provides better predictive accuracy for the dry matter content of silage corn feed than the other 
models. 

 

Figure 8. Scatter Plot of SNV-CARS-PSO-ELM Model Performance 

 

Figure 9. Scatter Plot of SNV-BOSS-PSO-ELM Model Performance 



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Figure 10. Scatter Plot of SNV-IRIV-PSO-ELM Model Performance 

 

4. Discussion 

In this study, 270 silage maize feed samples were analyzed, focusing on the dry matter physicochemical values. 
The raw spectral data were preprocessed using five methods: SGF, MAF, MSC, SNV, and FD. Based on an 
analysis of model performance, the most suitable preprocessing method was selected for subsequent modeling. 
Characteristic wavelengths were selected using the CARS, BOSS, and IRIV algorithms, while prediction models 
were constructed based on full wavelengths and characteristic wavelengths using the ELM algorithm. The PSO 
algorithm was employed to optimize the modeling parameters during this process. The main conclusions are 
summarized as follows: 

(1) By comparing the spectral preprocessing results of SGF, MAF, MSC, SNV, and FD and combining them with 
the quantitative evaluation of the PSO-ELM model, it was found that SNV preprocessing yielded the best 
performance in the quantitative dry matter model. For the calibration set, the correlation coefficient (R) was 
0.8723, with a root mean square error (RMSE) of 0.7529. For the prediction set, the R was 0.8413, and the RMSE 
was 0.768. These results indicate that SNV preprocessing effectively reduces the effects of spectral scattering, 
baseline drift, and background interference, thereby enhancing the consistency and reliability of the spectral data 
and significantly improving the accuracy and stability of the model. 

(2) The characteristic wavelength screening focused on the range of 1100–1800 nm, which encompasses the core 
absorption regions of the primary chemical components in dry matter, including the vibrational absorption regions 
of the O-H bond in water and the C-H bond in cellulose and hemicellulose. Among the algorithms, BOSS 
demonstrated a broader and more balanced wavelength distribution, comprehensively covering key intervals and 
enhancing the model's generalization ability. CARS concentrated on key absorption regions, producing results 
with strong relevance to the target, though slightly limited in the breadth of information covered. IRIV, on the 
other hand, covered a wider wavelength range and captured the features across multiple intervals but included a 
higher number of redundant variables, which negatively impacted model efficiency. 

(3) In the evaluation of the models built using characteristic wavelengths, the BOSS-based PSO-ELM model 
demonstrated the best overall performance. For the quantitative dry matter model, the BOSS-based model achieved 
an R of 0.9142 and an RMSE of 0.6306 in the calibration set, while in the prediction set, the R was 0.8708 and the 
RMSE was 0.6802. These results outperformed other algorithms, indicating that BOSS effectively balances model 
prediction performance and improves the correlation coefficient while maintaining high accuracy. 

Acknowledgments 

The research is financed by Key Laboratory of Modern Agricultural Equipment and Technology Project 
(MAET202322) and National Natural Science Foundation of China Youth Fund Project (61901194). 

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Copyrights 

Copyright for this article is retained by the author(s), with first publication rights granted to the journal. 

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    /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.)
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    /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing.  Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
  >>
  /Namespace [
    (Adobe)
    (Common)
    (1.0)
  ]
  /OtherNamespaces [
    <<
      /AsReaderSpreads false
      /CropImagesToFrames true
      /ErrorControl /WarnAndContinue
      /FlattenerIgnoreSpreadOverrides false
      /IncludeGuidesGrids false
      /IncludeNonPrinting false
      /IncludeSlug false
      /Namespace [
        (Adobe)
        (InDesign)
        (4.0)
      ]
      /OmitPlacedBitmaps false
      /OmitPlacedEPS false
      /OmitPlacedPDF false
      /SimulateOverprint /Legacy
    >>
    <<
      /AddBleedMarks false
      /AddColorBars false
      /AddCropMarks false
      /AddPageInfo false
      /AddRegMarks false
      /ConvertColors /ConvertToCMYK
      /DestinationProfileName ()
      /DestinationProfileSelector /DocumentCMYK
      /Downsample16BitImages true
      /FlattenerPreset <<
        /PresetSelector /MediumResolution
      >>
      /FormElements false
      /GenerateStructure false
      /IncludeBookmarks false
      /IncludeHyperlinks false
      /IncludeInteractive false
      /IncludeLayers false
      /IncludeProfiles false
      /MultimediaHandling /UseObjectSettings
      /Namespace [
        (Adobe)
        (CreativeSuite)
        (2.0)
      ]
      /PDFXOutputIntentProfileSelector /DocumentCMYK
      /PreserveEditing true
      /UntaggedCMYKHandling /LeaveUntagged
      /UntaggedRGBHandling /UseDocumentProfile
      /UseDocumentBleed false
    >>
  ]
>> setdistillerparams
<<
  /HWResolution [2400 2400]
  /PageSize [612.000 792.000]
>> setpagedevice

