Early determination of mildew status in storage maize kernels using hyperspectral imaging combined with the stacked sparse auto-encoder algorithm

Yang, Dong; Yuan, Jianghao; Chang, Qing; Zhao, Huiyi; Cao, Yang

doi:10.1016/j.infrared.2020.103412

Cited by 26 publications

(9 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overall structure diagram is shown in Figure 4. The blue neurons represent the activated unit, and the gray neurons represent the unactivated unit (the values are close to zero) (Yang et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Identification of maize seed varieties based on stacked sparse autoencoder and near‐infrared hyperspectral imaging technology

Sun

Wang

et al. 2022

J Food Process Engineering

View full text Add to dashboard Cite

Maize seed variety identification is the key to improving the quality and yield of maize. The study aimed to investigate a stacked sparse autoencoder combined with a cuckoo search (CS) optimized support vector machine (SSAE-CS-SVM) to meet the identification requirements of accurate detection. First, the near-infrared (NIR) (871.61-1766.32 nm) hyperspectral data of maize seeds were processed using Savitzky-Golay (SG) combined with standard normal variables (SNV). Subsequently, SSAE, SAE, principal component analysis (PCA), and competitive adaptive reweighted sampling were employed for feature extraction. Finally, the recognition model was constructed using the SoftMax regression model, SVM, and CS optimized SVM (CS-SVM). The results indicated that the SSAE-CS-SVM model achieved satisfactory performance (the training set and testing set accuracies were 99.45% and 95.81%, respectively). This study confirmed the great potential of combining NIR hyperspectral imaging technology with deep learning algorithms for maize seed variety identification. Practical applicationsThe identification of maize seed varieties is extremely important for ensuring seed purity and improving maize quality and yield. A method based on SSAE and NIR hyperspectral imaging technology was studied to identify maize seed varieties. The method proposed in this paper could identify maize seed varieties non-destructively and accurately, which provided a new way for the online detection of seed varieties.

show abstract

Section: Methodsmentioning

confidence: 99%

Identification of maize seed varieties based on stacked sparse autoencoder and near‐infrared hyperspectral imaging technology

Sun

Wang

et al. 2022

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…Zhu et al proposed a new method for aflatoxin B 1 (AFB 1 ) detection inspired by quantitative remote sensing [ 25 ]. Yang et al used hyperspectral imaging (HSI) combined with the deep stacked sparse auto-encoders (SSAE) algorithm to recognize the early mildewed degree of kernels [ 26 ]. Han et al realized pixel-level aflatoxin detection based on deep learning and hyperspectral imaging [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize

Wang

Deng

Jiang

2022

Foods

View full text Add to dashboard Cite

This work provides a novel approach to monitor the aflatoxin B1 (AFB1) content in maize by near-infrared (NIR) spectra-based deep learning models that integrates Markov transition field (MTF) image coding and a convolutional neural network (CNN) strategy. According to the data structure characteristics of near-infrared spectra, new structures of one-dimensional CNN (1D-CNN) and two-dimensional MTF-CNN (2D-MTF-CNN) were designed to construct a deep learning model for the monitoring of AFB1 in maize. The results obtained showed that compared with the 1D-CNN model, the performance of the 2D-MTF-CNN model had been significantly improved, and its root mean square error of prediction, coefficient of predictive determination, and relative percent deviation were 1.3591 μg·kg−1, 0.9955, and 14.9386, respectively. The results indicate that the MTF is an effective data encoding technique for converting one-dimensional spectra into two-dimensional images. It more intuitively reflects the intrinsic characteristics of the NIR spectra from a new perspective and provides richer spectral information for the construction of deep learning models, which can ensure the detection accuracy and generalization performance of deep learning quantitative detection models. This study provides a new analytical perspective for the chemometrics analysis of the NIR spectroscopy.

show abstract

“…Besides, multivariate data analysis can help uncover useful information hidden within it ( Maione et al, 2019 ), especially for massive datasets from sensors. Machine learning methods showed the excellent data mining ability in hyperspectral data mining ( Yang et al, 2020 ; Najafabadi, 2021 ; Weng et al, 2021 ), and the combination between them can be exploited as a competent tool in plant science ( Greener et al, 2022 ) such as early stress detection ( Gu et al, 2019 ; Lu et al, 2020 ; Zheng et al, 2020 ), unsound kernel identification ( Liang et al, 2020 ; Zhang et al, 2021a ), and the evaluation of nutrition content ( Zhang et al, 2020a , b ; Najafabadi, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

Tao

Bai

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other’s advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection.

show abstract

Early determination of mildew status in storage maize kernels using hyperspectral imaging combined with the stacked sparse auto-encoder algorithm

Cited by 26 publications

References 44 publications

Identification of maize seed varieties based on stacked sparse autoencoder and near‐infrared hyperspectral imaging technology

Identification of maize seed varieties based on stacked sparse autoencoder and near‐infrared hyperspectral imaging technology

Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

Contact Info

Product

Resources

About