scholarly journals The Application Progress and Prospect of Miniature Spectrometer in Precision Agriculture

CONVERTER ◽  
2021 ◽  
pp. 658-668
Author(s):  
Hui Zhi, Jianrang Luo, Ruiwen Liu
Keyword(s):  
Precision Agriculture ◽  
High Performance ◽  
Near Infrared ◽  
High Efficiency ◽  
Research Progress ◽  
Non Destructive Testing ◽  
Single Chip ◽  
Destructive Testing ◽  
Non Destructive ◽  
At Home

Miniature spectrometers are widely used in various fields such as industry and agriculture thanks to their advantages of easy portability, non-destructive testing, online testing, and high efficiency. By surveying massive research results at home and abroad, this paper summarizes the current development status of miniature near-infrared spectrometers at home and abroad, emphatically introduces the application research progress of miniature spectrometers in precision agriculture. Finally, it outlooks the development prospects of miniature spectrometers. While improving specificity, miniature spectrometers are developing towards high performance, new principles, and single chip.

Review on Non-Destructive Testing Technique of Eddy Current Pulsed Thermography

2015 ◽  
Vol 742 ◽  
pp. 128-131 ◽  
Author(s):  
Jian Min Zhou ◽  
Jun Yang ◽  
Qi Wan
Keyword(s):  
Eddy Current ◽  
Signal Reconstruction ◽  
Infrared Image ◽  
Principal Component ◽  
Research Progress ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Pulsed Thermography ◽  
Pulsed Phase Thermography ◽  
Non Destructive

This paper introduces the theory of eddy current pulsed thermography and expounds the research status of eddy current pulsed thermography in application and information extraction. Thermographic signal reconstruction, pulsed phase thermography, principal component analysis were introuduced in this paper and listed some fusion multiple methods to acquire information from infrared image. At last, it summarizes research progress, existing problem and deelopment of eddy current pulsed thermography.

Development of a Portable near Infrared Sugar-Measuring Instrument

2002 ◽  
Vol 10 (1) ◽  
pp. 77-83 ◽  
Author(s):  
Tsuyoshi Temma ◽  
Kenkoh Hanamatsu ◽  
Fujitoshi Shinoki
Keyword(s):  
Fruits And Vegetables ◽  
Near Infrared ◽  
Sugar Content ◽  
Processing Unit ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
General Chemical ◽  
The Difference ◽  
Brix Value ◽  
Non Destructive

Industries from agriculture to petrochemistry have found near infrared (NIR) spectroscopic analysis useful for quality control and quantitative analysis of materials and products. The general chemical, polymer chemistry, petrochemistry, agriculture, food and textile industries are currently using NIR spectroscopic methods for analysis. In this study, we developed a portable NIR instrument for the non-destructive testing of products in the field, which has resulted in an instrument for commercial sale and use. The instrument consists of a light source, a polychromator, a wave-guide (optical fibre bundle) and a data processing unit. We tested the performance of the portable NIR instrument in determining the sugar content of apples. The performance was also examined at full width at half maximum ( FWHM) of the spectrum. The difference in the absorption of quartz and plastic fibres in the NIR was also compared. The sugar content measurements were confirmed by a high correlation to the Brix value of the apples, and the calibration showed the accuracy of the instrument in practice. Application of this instrument to fruits and vegetables other than apples was explored.

High-Efficiency Remote Measurement of Pipe Defect Using RF/UT Technologies: A Theoretical Analysis Part One — Straight Beam UT

2016 ◽  
Author(s):  
Wissam M. Alobaidi ◽  
Eric Sandgren
Keyword(s):  
High Efficiency ◽  
Pipe Wall ◽  
Remote Measurement ◽  
Non Destructive Testing ◽  
Step Width ◽  
Destructive Testing ◽  
Signal Velocity ◽  
Straight Beam ◽  
Non Destructive ◽  
Calibration Condition

This analysis has established a new hybrid RF/UT system for non-destructive testing of pipe walls for pipe wall thinning (PWT) in order to predict location, and enable measurement of the depth of defect by combining the group velocity method and calibration condition. A simulation of microwave (MW) behavior in a 91% brass waveguide (762mm pipe, Young’s Modulus 102KN/mm2) was developed using Computer Simulation Technology (CST). The model included a frequency band of 1.283GHz for the TMnm mode (TM01 and TM21), with a sweeping frequency from 0.70GHZ to 2.00GHz. The model includes 14 instances of full-circumferential PWT, regularly spaced along the length of the waveguide with step-width of 50.8mm on center. For each we have modeled four cases of increasing PWT (5.08mm, 10.16mm, 15.24mm and 20.32mm). Considering the measurement with MW as a prediction of the location of the PWT, rather than a measurement, we can guide a straight-beam UT probe to the position predicted by MW, and use the appropriate signal velocity ultrasound to accurately measure the depth to defect from the outer surface of the pipe. The straight beam UT is found to be no better at determining the geometry of the defect than MW, but the accurate depth to defect (DDO) measurement would allow estimation of the volume of the PWT.

Data augmentation by CycleGAN-based extra-supervised for non-destructive testing

2021 ◽  
Author(s):  
Jiangshan Ai ◽  
Lulu Tian ◽  
Libing Bai ◽  
Jie Zhang
Keyword(s):  
High Performance ◽  
Field Data ◽  
Large Scale ◽  
Data Augmentation ◽  
Learning Method ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Non Destructive

Abstract Deep learning method is widely used in computer vision tasks with large scale annotated datasets. However, it is a big challenge to obtain such datasets in most directions of the vision based non-destructive testing (NDT) field. Data augmentation is proved as an efficient way in dealing with the lack of large-scale annotated datasets. In this paper, we propose CycleGAN-based extra-supervised (CycleGAN-ES) to generate synthetic NDT images, where the ES is used to ensure that the bidirectional mapping are learned for corresponding label and defect. Furthermore, we show the effectiveness of using the synthesized images to train deep convolutional neural networks (DCNN) for defects recognition. In the experiments, we extract numbers of X-ray welding images with both defect and no-defect from the published GDXray dataset, CycleGAN-ES are used to generate the synthetic defect images based on a small number of extracted defect images and manually drawn labels which are used as a content guide. For quality verification of the synthesized defect images, we use a high-performance classifier pre-trained using big dataset to recognize the synthetic defects and show comparability of the performances of classifiers trained using synthetic defects and real defects respectively. To present the effectiveness of using the synthesized defects as an augmentation method, we train and evaluate the performances of DCNN for defects recognition with or without the synthesized defects.

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