Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM

Yang, Jun; Sun, Laijun; Wang, Xing; Feng, Guojun; Bai, Hongyi; Wang, Jiaying

doi:10.1016/j.saa.2021.119585

Cited by 30 publications

(18 citation statements)

References 20 publications

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“…We also found that with an increase in common hybrid varieties, the performance of the SVM or MLP model using full wavelength data performed significantly better than that using characteristic feature wavelengths. As shown in the study of Yang et al [ 46 ], the prediction accuracy of sugarbeet seeds SVM model based on 16 characteristic wavelengths reduced by 3.18% than that of full wavelength. This difference in performance could be related to the selection of feature wavelengths from the original training data, which could overlook informative wavelengths needed to discern other varieties [ 24 , 47 ].…”

Section: Discussionmentioning

confidence: 95%

“…One common way to select variables is the successive projections algorithm (SPA) approach, selecting several typical characteristic wavelengths that predict the output, without mathematical transformations on the raw reflectance data [ 18 ]. As a forward selection method, SPA is based on the principle of root mean square error (RMSE) minimization [ 46 , 51 ]. It selects the variable with the lowest collinearity and redundancy.…”

Section: Methodsmentioning

confidence: 99%

“…RF uses the decision tree as the base classifier to resample the same data set and establish multiple similar base classifiers. The classification prediction results of these base classifiers with slight differences can output the overall classification results by using integration methods such as averaging or voting [ 46 ]. This study used the RBF kernel to construct a nonlinear SVM model in the spectral analysis [ 17 , 51 ].…”

Section: Methodsmentioning

confidence: 99%

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A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

Wen

Cheng

et al. 2022

Plant Methods

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Background Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data. Results Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties. Conclusions This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.

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Section: Discussionmentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

Wen

Cheng

et al. 2022

Plant Methods

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“…The successive projection algorithm (SPA) has been successfully applied in many studies on the dimensionality reduction processing of vegetation hyperspectral features [39][40][41][42]. SPA can overcome the collinearity between sensitive bands, select important wavelengths, and establish reliable models.…”

Section: Methodsmentioning

confidence: 99%

Detection of Larch Forest Stress from Jas’s Larch Inchworm (Erannis jacobsoni Djak) Attack Using Hyperspectral Remote Sensing

Huang

Xie

et al. 2021

Remote Sensing

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Detection of forest pest outbreaks can help in controlling outbreaks and provide accurate information for forest management decision-making. Although some needle injuries occur at the beginning of the attack, the appearance of the trees does not change significantly from the condition before the attack. These subtle changes cannot be observed with the naked eye, but usually manifest as small changes in leaf reflectance. Therefore, hyperspectral remote sensing can be used to detect the different stages of pest infection as it offers high-resolution reflectance. Accordingly, this study investigated the response of a larch forest to Jas’s Larch Inchworm (Erannis jacobsoni Djak) and performed the different infection stages detection and identification using ground hyperspectral data and data on the forest biochemical components (chlorophyll content, fresh weight moisture content and dry weight moisture content). A total of 80 sample trees were selected from the test area, covering the following three stages: before attack, early-stage infection and middle- to late-stage infection. Combined with the Findpeaks-SPA function, the response relationship between biochemical components and spectral continuous wavelet coefficients was analyzed. The support vector machine classification algorithm was used for detection infection. The results showed that there was no significant difference in the biochemical composition between healthy and early-stage samples, but the spectral continuous wavelet coefficients could reflect these subtle changes with varying degrees of sensitivity. The continuous wavelet coefficients corresponding to these stresses may have high potential for infection detection. Meanwhile, the highest overall accuracy of the model based on chlorophyll content, fresh weight moisture content and dry weight moisture content were 90.48%, 85.71% and 90.48% respectively, and the Kappa coefficients were 0.85, 0.79 and 0.86 respectively.

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“…Baek et al developed an SVM model to detect those rice seeds stained with lesions. The results showed that it was feasible to screen diseased rice seeds based on ML algorithms and spectral imaging technology [17]. Pattern recognition technology and data mining methods have become hotspots in chemometrics.…”

Section: Introductionmentioning

confidence: 99%

SVM Classification Method of Waxy Corn Seeds with Different Vitality Levels Based on Hyperspectral Imaging

et al. 2022

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The vitality of corn seeds is a significant indicator for assessing the quality and yield of crops. In recent years, numerous information technologies have been adopted to analyze the seed vitality and provide support for efficient equipment. However, there are still some shortcomings in these technologies, which decrease the accuracy of identifying the seed vitality for various practical applications. In this paper, a synthesized classification method for seed vitality was proposed based on multisensor hyperspectral imaging. Firstly, hyperspectral images in the range of 370-1042 nm were collected for waxy corn seeds, which were subjected to aging processing with four periods of time (0, 3, 6, and 9 d). Besides, some preprocessing techniques including standard normal variate, multiplicative scatter correction, Savitzky-Golay smoothing, and first-order and second-order derivatives were employed to suppress noise interference in raw spectra. In addition, principal component analysis (PCA), 2nd derivatization, and successive projection algorithm (SPA) were adopted to select feature wavelengths. Moreover, SVM classification models based on full spectra and feature wavelengths were established. The results showed that, based on feature wavelengths selected by SPA, the SVM model preprocessed by multiplicative scatter correction (MSC) had the optimal performance. The training accuracy and testing accuracy of this model were 100% and 97.9167%, respectively. RMSE was 0.018 and R 2 was 0.875. Therefore, it can be demonstrated that the pattern recognition algorithm could achieve a high accuracy in classifying accelerated aging seeds. This algorithm provides a new method for machine learning (ML) in nondestructive detection of crops.

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Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM

Cited by 30 publications

References 20 publications

A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

Detection of Larch Forest Stress from Jas’s Larch Inchworm (Erannis jacobsoni Djak) Attack Using Hyperspectral Remote Sensing

SVM Classification Method of Waxy Corn Seeds with Different Vitality Levels Based on Hyperspectral Imaging

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