Intelligent detection of hard seeds of snap bean based on hyperspectral imaging

Wang, Jiaying; Sun, Laijun; Feng, Guojun; Bai, Hongyi; Yang, Jun; Gai, Zhaodong; Zhao, Zhide; Zhang, Guanghui

doi:10.1016/j.saa.2022.121169

Cited by 14 publications

(11 citation statements)

References 23 publications

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“…Few researchers have proposed that specific-wavelength bands in the visible light region may be related to plant pigments, such as chlorophyll II a at 430 nm, chlorophyll II b at 448 nm, carotenoids at approximately 448 nm and 471 nm, and anthocyanin at 623 nm, 642 nm, and 646 nm with absorption peaks ( Sun et al., 2016 ; Zhang et al., 2020 ). Additionally, the spectral band in the range of 400–500 nm is related to the starch content of seeds, and the band at approximately 900–1,000 nm is considered to reflect the difference in seed protein content ( Wang et al., 2022 ). The genes of the paternal parent influence the endosperm of the hybrid, and the starch and protein content of the hybrid showed few differences from the maternal parent, which were reflected in the corresponding bands of the spectrum.…”

Section: Resultsmentioning

confidence: 99%

“…It is related to the precipitation of pigment content in the aleurone layer. The 910-1000nm band corresponds to the protein content of seeds ( Wang et al., 2022 ), which reflects the difference in components between the hybrid and the female parent due to the genetic influence of the two parents. After feature screening, the model can still maintain a high prediction accuracy, reduce the number of spectra, and provide a reference for future multispectral detection.…”

Section: Resultsmentioning

confidence: 99%

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Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology

Zhang

Hou

Luo³

et al. 2022

Front. Plant Sci.

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Chemical hybridization and genic male sterility systems are two main methods of hybrid wheat production; however, complete sterility of female wheat plants cannot be guaranteed owing to the influence of the growth stage and weather. Consequently, hybrid wheat seeds are inevitably mixed with few parent seeds, especially female seeds. Therefore, seed purity is a key factor in the popularization of hybrid wheat. However, traditional seed purity detection and variety identification methods are time-consuming, laborious, and destructive. Therefore, to establish a non-destructive classification method for hybrid and female parent seeds, three hybrid wheat varieties (Jingmai 9, Jingmai 11, and Jingmai 183) and their parent seeds were sampled. The transmittance and reflectance spectra of all seeds were collected via hyperspectral imaging technology, and a classification model was established using partial least squares-discriminant analysis (PLS-DA) combined with various preprocessing methods. The transmittance spectrum significantly improved the classification of hybrids and female parents compared to that obtained using reflectance spectrum. Specifically, using transmittance spectrum combined with a characteristic wavelength-screening algorithm, the Detrend-CARS-PLS-DA model was established, and the accuracy rates in the testing sets of Jingmai 9, Jingmai 11, and Jingmai 183 were 95.69%, 98.25%, and 97.25%, respectively. In conclusion, transmittance hyperspectral imaging combined with a machine learning algorithm can effectively distinguish female parent seeds from hybrid seeds. These results provide a reference for rapid seed purity detection in the hybrid production process. Owing to the non-destructive and rapid nature of hyperspectral imaging, the detection of hybrid wheat seed purity can be improved by online sorting in the future.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology

Zhang

Hou

Luo³

et al. 2022

Front. Plant Sci.

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“…The reason is that this wavelength corresponds to the vibration of N-H chemical bond of amino acid in seeds, which can be used to verify the difference of amino acid content in AMM and AM seeds. ( Yang et al., 2021a ; Wang et al., 2022 ) In addition, there are four absorption peaks in the average reflectance spectra of AMM and AM seeds (valleys at 415 nm, 640, 680 and 885 nm). The carotenoid ( Yang et al., 2021a ) and proanthocyanidin content of the seed coat ( Wang et al., 2022 ) can be determined at about 415 nm; The bands at about 640 nm and 680 nm may be associated with the absorption of chlorophyll b and chlorophyll a ( Zhang et al., 2016 ).…”

Section: Resultsmentioning

confidence: 99%

Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds

Chen

et al. 2022

Front. Plant Sci.

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The roots of Astragalus membranaceus var. mongholicus (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine. Although AMM has higher yields and accounts for a larger market share, its cultivation is fraught with challenges, including mixed germplasm resources and widespread adulteration of commercial seeds. Current methods for distinguishing Astragalus seeds from similar (SM) seeds are time-consuming, laborious, and destructive. To establish a non-destructive method, AMM, AM, and SM seeds were collected from various production areas. Machine vision and hyperspectral imaging (HSI) were used to collect morphological data and spectral data of each seed batch, which was used to establish discriminant models through various algorithms. Several preprocessing methods based on hyperspectral data were compared, including multiplicative scatter correction (MSC), standard normal variable (SNV), and first derivative (FD). Then selection methods for identifying informative features in the above data were compared, including successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS). The results showed that support vector machine (SVM) modeling of machine vision data could distinguish Astragalus seeds from SM with >99% accuracy, but could not satisfactorily distinguish AMM seeds from AM. The FD-UVE-SVM model based on hyperspectral data reached 100.0% accuracy in the validation set. Another 90 seeds were tested, and the recognition accuracy was 100.0%, supporting the stability of the model. In summary, HSI data can be applied to discriminate among the seeds of AMM, AM, and SM non-destructively and with high accuracy, which can drive standardization in the Astragalus production industry.

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“…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%

“…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 ]. It carried out the five-fold cross-validation operation and grid search program to calculate optimal penalty coefficient c and the kernel parameter g .…”

Section: Methodsmentioning

confidence: 99%

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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Intelligent detection of hard seeds of snap bean based on hyperspectral imaging

Cited by 14 publications

References 23 publications

Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology

Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology

Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds

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

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