Identification of rice-weevil (Sitophilus oryzae L.) damaged wheat kernels using multi-angle NIR hyperspectral data

Zhang, Liu; Sun, Heng; Li, Hao; Rao, Zhenhong; Ji, Haiyan

doi:10.1016/j.jcs.2021.103313

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…LDA is a powerful supervised learning technique that can significantly increase the discrimination ability between classes based on the distance between projections and effectively classify data [38]. LDA pays more attention to the inter-class distance and intra-class distance of the projected samples in the new dimension space, ensuring that the model has the best separability in the subspace [39]. The method used in this study to solve the LDA hyperplane eigenmatrix is singular value decomposition (SVD), and the threshold used for rank estimation in the SVD solver is 1 × 10 −4 .…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning

Sun

et al. 2022

Foods

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Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning

Sun

et al. 2022

Foods

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“…Feature selection, another kind of method for reducing dimensionality, selects a feature subset from the original features, which can well maintain the original physical meaning of the features. Zhang et al used the random frog (RF) to perform feature selection on the infrared hyperspectral data of rice, which is joined by a machine learning model to identify rice grains damaged by rice weevil [24]. Zhu et al adopted the competitive adaptive reweighted sampling (CARS) method to select features from hyperspectral data and combined it with a classifier to identify soybean seeds [25].…”

Section: Introductionmentioning

confidence: 99%

Locality Preserved Selective Projection Learning for Rice Variety Identification Based on Leaf Hyperspectral Characteristics

Long,

Wen,

Deng

et al. 2023

Agronomy

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Rice has an important position in China as well as in the world. With the wide application of rice hybridization technology, the problem of mixing between individual varieties has become more and more prominent, so the variety identification of rice is important for the agricultural production, the phenotype collection, and the scientific breeding. Traditional identification methods are highly subjective and time-consuming. To address this issue, we propose a novel locality preserved selective projection learning (LPSPL) method for non-destructive rice variety identification based on leaf hyperspectral characteristics. The proposed LPSPL method can select the most discriminative spectral features from the leaf hyperspectral characteristics of rice, which is helpful to distinguish different rice varieties. In the experiments, support vector machine (SVM) is adopted to conduct the rice variety identification based on the selected spectral features. The experimental results show that the proposed method here achieves higher identification rates, 96% for the early rice and 98% for the late rice, respectively, which are superior to some state-of-the-art methods.

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“…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.

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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.

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Identification of rice-weevil (Sitophilus oryzae L.) damaged wheat kernels using multi-angle NIR hyperspectral data

Cited by 18 publications

References 25 publications

Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning

Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning

Locality Preserved Selective Projection Learning for Rice Variety Identification Based on Leaf Hyperspectral Characteristics

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

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