Spectral inversion model of the crushing rate of soybean under mechanized harvesting

Chen, Man; Ni, Youliang; Jin, Chengqian; LIU, Zheng; XU, Jinshan

doi:10.1590/fst.123221

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…The characteristic spectral intervals of fresh tea leaves were screened by the backward interval partial least squares method (biPLS) (Nørgaard et al, 2000), and then the principal component analysis (PCA) was conducted on the characteristic spectral data (Shahdoosti & Ghassemian, 2016). Finally, the least squares support vector machine (LS-SVM) (Chen et al, 2022) was used to establish a NIRS discrimination model for fresh tea leaves at different altitudes, and the robustness of the model was tested by prediction set samples, so as to provide a scientific, objective and convenient new method for the discrimination of fresh tea leaves at different altitudes. And it will also lays a solid technical foundation for the fair acquisition of fresh tea leaves.…”

Section: Fast and Nondestructive Discrimination Of Fresh Tea Leaves A...mentioning

confidence: 99%

“…In view of the good nonlinear performance of the radial basis function (RBF) kernel function, when using RBF kernel function to establish LS-SVM model (Chen et al, 2022), the key was to select appropriate super parameters γ and σ 2 . Super parameter γ was used to control model complexity and approximation error; Hyperparameter σ (bandwidth coefficient of kernel function) had an important influence on the measurement accuracy of the model.…”

Section: Establishment Of Ls-svm Modelmentioning

confidence: 99%

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Fast and nondestructive discrimination of fresh tea leaves at different altitudes based on near infrared spectroscopy and various chemometrics methods

et al. 2023

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Near infrared spectroscopy (NIRS) combined with various chemometrics methods was tried to identify the fresh tea leaves at different altitudes quickly and nondestructively. Three kinds of samples were collected, then scanning NIRS, conducting spectral preprocessing to remove noise information, using backward interval partial least squares to screen characteristic spectral intervals, going on principal component analysis, respectively. Finally, least squares support vector machine method (LS-SVM) was applied to establish NIRS models, whose robustness was tested by prediction set samples. The best pretreated method was the combination of multivariate scattering correction and the first derivative. Six characteristic spectral intervals were screened, and the corresponding spectral wavenumbers were 4821.

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Section: Fast and Nondestructive Discrimination Of Fresh Tea Leaves A...mentioning

confidence: 99%

Section: Establishment Of Ls-svm Modelmentioning

confidence: 99%

Fast and nondestructive discrimination of fresh tea leaves at different altitudes based on near infrared spectroscopy and various chemometrics methods

et al. 2023

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“…Zou et al (2022) used hyperspectral nondestructive testing technology to predict peanut seed vigor with high accuracy (Zou et al, 2022). Chen et al (2022) used hyperspectral technology combined with the inversion model of LS-SVM to achieve rapid online monitoring of soybean breakage rate by combine harvesters (Chen et al, 2022). Wang et al (2021) used multi-spectral technology to model the physical and chemical properties of green vegetables, and used a variety of machine learning algorithm models to analyze and predict Brix and pH values (Wang et al, 2021).…”

Section: Identification Of Peanut Storage Period Based On Hyperspectr...mentioning

confidence: 99%

Identification of peanut storage period based on hyperspectral imaging technology

et al. 2022

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Peanut storage time affected the quality of peanut seed sowing and germination and also affected the taste of edible peanuts. With the increase of peanut storage time, the total amount of water and amino acids decreased, and peanuts appeared moldy. The artificial judgment of peanut storage time mostly relied on visual classification to evaluate the color, which leads to large differences in color classifications between observers. This research was conducted to determine the fresh state of peanuts during storage based on the hyperspectral imaging (HSI) technology, and to identify the storage time of peanuts through hyperspectral images (387~1035 nm). Three models, two preprocessing methods, and two feature band extraction methods were combined. The experimental results shows that the DT-MF-Catboost model was the best method to detect the storage time of peanuts, and its accuracy of identifying the storage time of peanuts was 97.53%. Studies have shown that HSI has great potential in classifying the freshness and identification of peanuts, and provides a basis for non-destructive testing classification as well as grading of peanuts during storage.

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“…They used an orthogonal test to analyze the effects of collision speed and contact radius on the maximum stress and displacement in the collision process (Dun et al, 2015). Chen et al studied the detection algorithm of soybean crushing rate and established a model of soybean crushing rate based on spectral data (Chen et al, 2022). Chen et al designed a soybean longitudinal flow double-spiral roller and studied the distribution pattern of threshed materials (Chen et al, 2020a, b).…”

Section: Introductionmentioning

confidence: 99%

Soybean crushing forms by mechanical harvesting and factors affecting the proportions of different forms

Jin

Chen

et al. 2023

Food Sci. Technol

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Aiming at the problem of serious damage in the process of soybean harvesting and production, this paper analyzes the soybean harvesting process, and determines the main crushing forms of soybeans: epidermal breakage, splitting into two flaps, and overall crushing. The influence of different operation links on soybean crushing during the harvesting process, such as header feeding auger, horizontal seed spiral conveyor, horizontal miscellaneous residue spiral conveyor, and threshing roller was studied. The influence of the operating parameters such as the forward speed of the harvester, threshing roller speed, and deflector angle on the soybean crushing form was studied experimentally. The results showed that, the degree of influence on the proportion of sepidermal breakage from high to low was the forward speed of the harvester, deflector angle and threshing roller speed. The degree of influence on the proportion of splitting into two flaps from high to low was the deflector angle, threshing roller speed and the forward speed of the harvester. The degree of influence on the proportion of the overall crushing from high to low was the forward speed of the harvester, threshing roller speed and deflector angle.The research results can provide a reference for the subsequent structural improvement of soybean harvester and optimization of operating parameters.

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Spectral inversion model of the crushing rate of soybean under mechanized harvesting

Cited by 6 publications

References 6 publications

Fast and nondestructive discrimination of fresh tea leaves at different altitudes based on near infrared spectroscopy and various chemometrics methods

Fast and nondestructive discrimination of fresh tea leaves at different altitudes based on near infrared spectroscopy and various chemometrics methods

Identification of peanut storage period based on hyperspectral imaging technology

Soybean crushing forms by mechanical harvesting and factors affecting the proportions of different forms

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