Reflectance spectroscopy with operator difference for determination of behenic acid in edible vegetable oils by using convolutional neural network and polynomial correction

Weng, Shizhuang; Chu, Zhaojie; Wang, Manqin; Han, Kuang-Wei; Zhu, Gongqin; Liu, Cunchuan; Li, Xinhua; Huang, Liusheng

doi:10.1016/j.foodchem.2021.130668

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…The pooling layer keeps the most important features while reducing the feature dimension to avoid overfitting. The full connection layer maps the resulting feature maps into a feature vector and generates a probability vector belonging to each class to achieve classification (Fazari et al, 2021;Weng et al, 2022). LeNet-5, a classical CNN, consists of two convolution layers, two pooling layers, two full connection layers, and one output layer (Priyadharshini et al, 2019).…”

Section: Image Featuresmentioning

confidence: 99%

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion

Weng

Ma²,

Tao³

et al. 2023

Front. Plant Sci.

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Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson’s correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses

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Section: Image Featuresmentioning

confidence: 99%

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion

Weng

Ma²,

Tao³

et al. 2023

Front. Plant Sci.

Self Cite

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“…Convolutional neural network (CNN) is one of the typical deep learning models. CNN has been proven effective in processing spectra data and establishing classification and regression models for various agricultural tasks ( Zhang et al., 2020b ; Zhang et al., 2020c ; Gai et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves

Xiao

Tang

et al. 2022

Front. Plant Sci.

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Leaf nitrogen concentration (LNC) is a critical indicator of crop nutrient status. In this study, the feasibility of using visible and near-infrared spectroscopy combined with deep learning to estimate LNC in cotton leaves was explored. The samples were collected from cotton’s whole growth cycle, and the spectra were from different measurement environments. The random frog (RF), weighted partial least squares regression (WPLS), and saliency map were used for characteristic wavelength selection. Qualitative models (partial least squares discriminant analysis (PLS-DA), support vector machine for classification (SVC), convolutional neural network classification (CNNC) and quantitative models (partial least squares regression (PLSR), support vector machine for regression (SVR), convolutional neural network regression (CNNR)) were established based on the full spectra and characteristic wavelengths. Satisfactory results were obtained by models based on CNN. The classification accuracy of leaves in three different LNC ranges was up to 83.34%, and the root mean square error of prediction (RMSEP) of quantitative prediction models of cotton leaves was as low as 3.36. In addition, the identification of cotton leaves based on the predicted LNC also achieved good results. These results indicated that the nitrogen content of cotton leaves could be effectively detected by deep learning and visible and near-infrared spectroscopy, which has great potential for real-world application.

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A deep learning approach to improving spectral analysis of fruit quality under interseason variation

et al. 2022

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Reflectance spectroscopy with operator difference for determination of behenic acid in edible vegetable oils by using convolutional neural network and polynomial correction

Cited by 7 publications

References 37 publications

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion

Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves

A deep learning approach to improving spectral analysis of fruit quality under interseason variation

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