An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange

The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky–Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of “Luogang” orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.

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Rapid Nondestructive Detection of Water Content and Granulation in Postharvest “Shatian” Pomelo Using Visible/Near-Infrared Spectroscopy

Biosensors ◽

10.3390/bios10040041 ◽

2020 ◽

Vol 10 (4) ◽

pp. 41

Author(s):

Sai Xu ◽

Huazhong Lu ◽

Christopher Ference ◽

Guangjun Qiu ◽

Xin Liang

Keyword(s):

Feature Extraction ◽

Data Processing ◽

Water Content ◽

Near Infrared ◽

Internal Quality ◽

Nondestructive Detection ◽

Information Mining ◽

Quality Detection ◽

Spectral Signal ◽

Validation Set

Visible/near-infrared (VIS/NIR) spectroscopy is a powerful tool for rapid, nondestructive fruit quality detection. This technology has been widely applied for quality detection of small, thin-peeled fruit, though less so for large, thick-peeled fruit due to a weak spectral signal resulting in a reduction of accuracy. More modeling work should be focused on solving this problem. “Shatian” pomelo is a traditional Chinese large, thick-peeled fruit, and granulation and water loss are two major internal quality factors that influence its storage quality. However, there is no efficient, nondestructive detection method for measuring these factors. Thus, the VIS/NIR spectral signal detection of 120 pomelo samples during storage was performed. Information mining (singular sample elimination, data processing, feature extraction) and modeling were performed in different ways to construct the optimal method for achieving an accurate detection. Our results showed that the water content of postharvest pomelo was optimally detected using the Savitzky–Golay method (SG) plus the multiplicative scatter correction method (MSC) for data processing, genetic algorithm (GA) for feature extraction, and partial least squares regression (PLSR) for modeling (the coefficient of determination and root mean squared error of the validation set were 0.712 and 0.0488, respectively). Granulation degree was best detected using SG for data processing and PLSR for modeling (the detection accuracy of the validation set was 100%). Additionally, our research showed a weak relationship between the pomelo water content and granulation degree, which provided a reference for the existing debates. Therefore, our results demonstrated that VIS/NIR combined with optimal information mining and modeling methodswas feasible for determining the water content and granulation degree of postharvest pomelo, and for providing references for the nondestructive internal quality detection of other large, thick-peeled fruits.

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Multi-source information fusion and deep-learning-based characteristics measurement for exploring the effects of peer engagement on stock price synchronicity

Information Fusion ◽

10.1016/j.inffus.2020.11.006 ◽

2021 ◽

Vol 69 ◽

pp. 1-21

Author(s):

Lin Li ◽

Feng Zhu ◽

Hui Sun ◽

Yiyi Hu ◽

Yunyun Yang ◽

...

Keyword(s):

Deep Learning ◽

Information Fusion ◽

Stock Price ◽

Source Information ◽

Stock Price Synchronicity ◽

Peer Engagement

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Identification of Abnormal Conditions for Fused Magnesium Melting Process Based on Deep Learning and Multi-source Information Fusion

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2021.3070512 ◽

2021 ◽

pp. 1-1

Author(s):

Ping Zhou ◽

Benhua Gao ◽

Shu Wang ◽

Tianyou Chai

Keyword(s):

Deep Learning ◽

Information Fusion ◽

Melting Process ◽

Source Information

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The Fusion Strategy of 2D and 3D Information Based on Deep Learning: A Review

Remote Sensing ◽

10.3390/rs13204029 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4029

Author(s):

Jianghong Zhao ◽

Yinrui Wang ◽

Yuee Cao ◽

Ming Guo ◽

Xianfeng Huang ◽

...

Keyword(s):

Deep Learning ◽

Information Fusion ◽

Information Integration ◽

Point Clouds ◽

Future Research ◽

Accuracy Improvement ◽

3D Point Clouds ◽

3D Information ◽

2D And 3D ◽

Research Domain

Recently, researchers have realized a number of achievements involving deep-learning-based neural networks for the tasks of segmentation and detection based on 2D images, 3D point clouds, etc. Using 2D and 3D information fusion for the advantages of compensation and accuracy improvement has become a hot research topic. However, there are no critical reviews focusing on the fusion strategies of 2D and 3D information integration based on various data for segmentation and detection, which are the basic tasks of computer vision. To boost the development of this research domain, the existing representative fusion strategies are collected, introduced, categorized, and summarized in this paper. In addition, the general structures of different kinds of fusion strategies were firstly abstracted and categorized, which may inspire researchers. Moreover, according to the methods included in this paper, the 2D information and 3D information of different methods come from various kinds of data. Furthermore, suitable datasets are introduced and comparatively summarized to support the relative research. Last but not least, we put forward some open challenges and promising directions for future research.

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Classification and Recognition of Electronic Components Based on Improved Cooperative Semi-supervised Learning Algorithm

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096514666201224125653 ◽

2020 ◽

Vol 14 ◽

Author(s):

Dan Luo

Keyword(s):

Deep Learning ◽

Machine Vision ◽

Supervised Learning ◽

Image Recognition ◽

Production Efficiency ◽

Learning Algorithm ◽

Electronic Components ◽

Electron Device ◽

Actual Recognition ◽

The Subject

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

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