Variable weighted convolutional neural network for the nitrogen content quantization of Masson pine seedling leaves with near-infrared spectroscopy

Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.

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Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2020.597864 ◽

2021 ◽

Vol 14 ◽

Author(s):

Kunqiang Qing ◽

Ruisen Huang ◽

Keum-Shik Hong

Keyword(s):

Neural Network ◽

Infrared Spectroscopy ◽

Convolutional Neural Network ◽

Near Infrared Spectroscopy ◽

Classification Accuracy ◽

Near Infrared ◽

Classification Performance ◽

Hemodynamic Responses ◽

Functional Near Infrared Spectroscopy ◽

Cerebral Hemodynamic

This study decodes consumers' preference levels using a convolutional neural network (CNN) in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy (fNIRS) is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the proposed method are the main advantages. The experimental procedure required eight healthy participants (four female and four male) to view commercial advertisement videos of different durations (15, 30, and 60 s). The cerebral hemodynamic responses of the participants were measured. To compare the preference classification performances, CNN was utilized to extract the most common features, including the mean, peak, variance, kurtosis, and skewness. Considering three video durations, the average classification accuracies of 15, 30, and 60 s videos were 84.3, 87.9, and 86.4%, respectively. Among them, the classification accuracy of 87.9% for 30 s videos was the highest. The average classification accuracies of three preferences in females and males were 86.2 and 86.3%, respectively, showing no difference in each group. By comparing the classification performances in three different combinations (like vs. so-so, like vs. dislike, and so-so vs. dislike) between two groups, male participants were observed to have targeted preferences for commercial advertising, and the classification performance 88.4% between “like” vs. “dislike” out of three categories was the highest. Finally, pairwise classification performance are shown as follows: For female, 86.1% (like vs. so-so), 87.4% (like vs. dislike), 85.2% (so-so vs. dislike), and for male 85.7, 88.4, 85.1%, respectively.

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Efficient Recognition and Automatic Sorting Technology of Waste Textiles Based on Online Near infrared Spectroscopy and Convolutional Neural Network

Resources Conservation and Recycling ◽

10.1016/j.resconrec.2022.106157 ◽

2022 ◽

Vol 180 ◽

pp. 106157

Author(s):

Wenqian Du ◽

Jiahui Zheng ◽

Wenxia Li ◽

Zhengdong Liu ◽

Huaping Wang ◽

...

Keyword(s):

Neural Network ◽

Infrared Spectroscopy ◽

Convolutional Neural Network ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Automatic Sorting ◽

Efficient Recognition

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Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks

Journal of Analytical Methods in Chemistry ◽

10.1155/2020/9652470 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Lei Zhang ◽

Xiangqian Ding ◽

Ruichun Hou

Keyword(s):

Neural Network ◽

Neural Networks ◽

Infrared Spectroscopy ◽

Convolutional Neural Network ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Two Dimensional ◽

One Dimensional ◽

Nirs Data ◽

The One

The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. There are many applications for the identification of tobacco origin by near-infrared spectroscopy. In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN. Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the pattern features of one-dimensional NIRS data, and then the extracted features are used for classification. Secondly, the one-dimensional NIRS data are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the two-dimensional convolutional neural network (2-D CNN) method. The classification is performed by the combination of global and local training features. Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. The multimodal NIR-CNN identification models of tobacco origin were established by using NIRS of 5,200 tobacco samples from 10 major tobacco producing provinces in China and 3 foreign countries. The classification accuracy of 1-D CNN and 2-D CNN models was 93.15% and 93.05%, respectively, which was better than the traditional PLS-DA method. The experimental results show that the application of 1-D CNN and 2-D CNN can accurately and reliably distinguish the NIRS data, and it can be developed into a new rapid identification method of tobacco origin, which has an important promotion value.

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1D convolutional neural network for the discrimination of aristolochic acids and their analogues based on near-infrared spectroscopy

Analytical Methods ◽

10.1039/c9ay01531k ◽

2019 ◽

Vol 11 (40) ◽

pp. 5118-5125 ◽

Cited By ~ 4

Author(s):

Xiaoyi Chen ◽

Qinqin Chai ◽

Ni Lin ◽

Xianghui Li ◽

Wu Wang

Keyword(s):

Neural Network ◽

Infrared Spectroscopy ◽

Convolutional Neural Network ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Aristolochic Acids ◽

End To End

An end-to-end 1D-CNN based on the NIRS technique is proposed to non-destructively and effectively discriminate aristolochic acids and their analogues.

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