Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network

Zhu, Susu; Zhou, Lei; Zhang, Chu; Bao, Yukun; Wu, Bangyu; Chu, Hangjian; Yu, Yue; He, Yong

doi:10.3390/s19194065

Cited by 39 publications

(16 citation statements)

References 33 publications

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“…Seed identification technology based on spectral reflectance has gradually developed, but image-based seed identification technology has not achieved satisfactory results. With the development of computer technology and information technology, the automatic identification of seed varieties and quality by machine vision combined with deep learning is an inevitable trend [20,63,64]. Moreover, hyperspectral technology is not limited to seed identification.…”

Section: Resultsmentioning

confidence: 99%

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Zhu

Chao

Zhang

et al. 2019

Sensors

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Hyperspectral imaging is a nondestructive testing technology that integrates spectroscopy and iconology technologies, which enables us to quickly obtain both internal and external information of objects and identify crop seed varieties. First, the hyperspectral images of ten soybean seed varieties were collected and the reflectance was obtained. Savitzky-Golay smoothing (SG), first derivative (FD), standard normal variate (SNV), fast Fourier transform (FFT), Hilbert transform (HT), and multiplicative scatter correction (MSC) spectral reflectance pretreatment methods were used. Then, the feature wavelengths and feature information of the pretreated spectral reflectance data were extracted using competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and principal component analysis (PCA). Finally, 5 classifiers, Bayes, support vector machine (SVM), k-nearest neighbor (KNN), ensemble learning (EL), and artificial neural network (ANN), were used to identify seed varieties. The results showed that MSC-CARS-EL had the highest accuracy among the 90 combinations, with training set, test set, and 5-fold cross-validation accuracies of 100%, 100%, and 99.8%, respectively. Moreover, the contribution of spectral pretreatment to discrimination accuracy was higher than those of feature extraction and classifier selection. Pretreatment methods determined the range of the identification accuracy, feature-selective methods and classifiers only changed within this range. The experimental results provide a good reference for the identification of other crop seed varieties.

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Section: Resultsmentioning

confidence: 99%

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Zhu

Chao

Zhang

et al. 2019

Sensors

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“…The source dataset in this study was much larger than all the datasets in their study and was enough for VGG-MODEL training. For structures like ResNet, Zhu et al (2019) compared the performance of a developed ResNet with a general deep convolutional neural network on a cotton dataset. Also, they found that ResNet was not as effective as the latter one.…”

Section: Classification Results On Source Datasetmentioning

confidence: 99%

Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

Liu

Meng

et al. 2021

Front. Bioeng. Biotechnol.

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Rapid varieties classification of crop seeds is significant for breeders to screen out seeds with specific traits and market regulators to detect seed purity. However, collecting high-quality, large-scale samples takes high costs in some cases, making it difficult to build an accurate classification model. This study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning. Three deep neural networks with typical structures were designed based on a sample-rich Pea dataset. Obtained the highest accuracy of 99.57%, VGG-MODEL was transferred to classify four target datasets (rice, oat, wheat, and cotton) with limited samples. Accuracies of the deep transferred model achieved 95, 99, 80.8, and 83.86% on the four datasets, respectively. Using training sets with different sizes, the deep transferred model could always obtain higher performance than other traditional methods. The visualization of the deep features and classification results confirmed the portability of the shared features of seed spectra, providing an interpreted method for rapid and accurate varieties classification of crop seeds. The overall results showed great superiority of HSI combined with deep transfer learning for seed detection under sample-limited condition. This study provided a new idea for facilitating a crop germplasm screening process under the scenario of sample scarcity and the detection of other qualities of crop seeds under sample-limited condition based on HSI.

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“…The source dataset in this study was much larger than all the datasets in [43] and was enough for VGG-MODEL training. For structure like ResNet, [44] compared the classification accuracy of a developed ResNet with a general deep convolutional neural network on cotton datasets, and also found that…”

Section: Identification Results Analysis On Source Datasetmentioning

confidence: 99%

Rapid and Accurate Varieties Identification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

Liu

Bao

et al. 2020

Preprint

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Background: Varieties identification of crop seeds is significant for breeders to screen out seeds with specific traits and for market regulators to detect seeds purity. Hyperspectral imaging technology provides a fast and non-destructive means for varieties identification. And deep learning algorithm is suitable for effective analysis of redundant spectral data. However, deep learning algorithms have serious big data dependency, while collecting high-quality large-scale samples was high-cost in many cases. This made it difficult to build an accurate identification model. This study aimed to explore a rapid and accurate method for varieties identification of different crop seeds under sample-limited condition based on hyperspectral imaging and deep transfer learning.Results: Three deep neural networks with typical structures were designed based on a samples-rich Pea dataset. Obtained the highest accuracy of 99.57 %, VGG-MODEL was transferred to classify four target datasets (Rice, Oat, Wheat, Cotton) with limited samples. The accuracies of deep transferred model achieved 95 %, 99 %, 80.8 %, and 83.86 % on the four datasets, respectively. Using training sets with different sizes, deep transferred model could always obtain higher performance than other traditional methods. Visualization of training process and classification results confirmed the portability of common features of seed spectra and provided an interpreted method for rapid and accurate varieties identification of crop seeds.Conclusions: This study combined hyperspectral imaging and deep transfer learning to identify varieties of different crop seeds, which was proved to be efficient under sample-limited condition. This facilitated crop variety screening process under the scenario of sample scarcity. It also provided a new idea for the detection of other qualities of crop seeds based on hyperspectral imaging under sample-limited condition.

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Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network

Cited by 39 publications

References 33 publications

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

Rapid and Accurate Varieties Identification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

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