Identification of maize seed varieties based on near infrared reflectance spectroscopy and chemometrics

Cui, Yongjin; Xu, Lanjun; An, Dong; Liu, Zhe; Gu, Jiancheng; Li, Shaoming; Zhang, Xiaodong; Zhu, Daming

doi:10.25165/j.ijabe.20181102.2815

Cited by 15 publications

(13 citation statements)

References 5 publications

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“…Near-infrared spectroscopy also has the advantage of minimum sample preparation and batch detection. Studies have proved the possibility to discriminant seed varieties using near-infrared spectroscopy [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties

et al. 2019

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Cotton seed purity is a critical factor influencing the cotton yield. In this study, near-infrared hyperspectral imaging was used to identify seven varieties of cotton seeds. Score images formed by pixel-wise principal component analysis (PCA) showed that there were differences among different varieties of cotton seeds. Effective wavelengths were selected according to PCA loadings. A self-design convolution neural network (CNN) and a Residual Network (ResNet) were used to establish classification models. Partial least squares discriminant analysis (PLS-DA), logistic regression (LR) and support vector machine (SVM) were used as direct classifiers based on full spectra and effective wavelengths for comparison. Furthermore, PLS-DA, LR and SVM models were used for cotton seeds classification based on deep features extracted by self-design CNN and ResNet models. LR and PLS-DA models using deep features as input performed slightly better than those using full spectra and effective wavelengths directly. Self-design CNN based models performed slightly better than ResNet based models. Classification models using full spectra performed better than those using effective wavelengths, with classification accuracy of calibration, validation and prediction sets all over 80% for most models. The overall results illustrated that near-infrared hyperspectral imaging with deep learning was feasible to identify cotton seed varieties.

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Section: Introductionmentioning

confidence: 99%

Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties

et al. 2019

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“…Molecular identification through DNA markers has the advantage of genetic stability and is independent of environmental conditions. But the cost of primer design is 2 of 16 high, and the identification process can damage the sample [4]. In summary, these detection methods are difficult to adapt to be online detection in the seed processing industry [5] and cannot complete the sorting of samples during processing.…”

Section: Introductionmentioning

confidence: 99%

Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Tan

Zhang

et al. 2022

Agriculture

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Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry.

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“…Morphology identification, physiochemistry analysis, and molecular identification are three types of conventional methods that may be used to identify sweet corn cultivars. These methods, including protein electrophoresis and DNA molecular markers, are time-consuming, costly, and destructive [9]. Thus, these methods may be used to detect a small group of sampling seeds.…”

Section: Introductionmentioning

confidence: 99%

“…Cui el at. (2018) established models based on FT-NIR data to identify maize varieties, and they obtained an average accuracy greater than 90% [9]. However, most of the studies could achieve applicable results using a batch of seeds as a sample.…”

Section: Introductionmentioning

confidence: 99%

Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

et al. 2019

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Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.

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Identification of maize seed varieties based on near infrared reflectance spectroscopy and chemometrics

Cited by 15 publications

References 5 publications

Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties

Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties

Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

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