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detection of spores using polarization image features and BP neural network

International Journal of Agricultural and Biological Engineering 2024年第5期17卷 213-221页

作者： Yafei Wang Ning Yang Guoxin Ma Mohamed Farag Taha Hanping Mao Xiaodong Zhang Qiang Shi School of Agricultural Engineering Jiangsu UniversityZhenjiang 212013JiangsuChina Jiangsu Changdian technology Co. Ltd.Jiangyin 214400JiangsuChina School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013JiangsuChina Department of Soil and Water Sciences Faculty of Environmental Agricultural SciencesArish UniversityNorth Sinai 45516Egypt School of Science and technology Shanghai Open UniversityShanghai 200433China

Timely detection and control of airborne disease is important to improve productivity. This study proposed a novel approach that utilizes micro polarization image features and a backpropagation neural network (BPNN) to classify and identify airborne disease spores in a greenhouse setting. Firstly, disease spores were collected in the greenhouse, and their surface morphological parameters were analyzed. Subsequently, the micropolarization imaging system for disease spores was established, and the micropolarization images of airborne disease spores from greenhouse crops were collected. Then the micropolarization images of airborne disease spores were processed, and the image features of polarization degree and polarization angle of disease spores were extracted. Finally, a disease spore classification model based on the BPNN was ultimately developed. The results showed that the texture position of the surface of the three disease spores was inconsistent, and the texture also showed an irregular shape. Texture information was present on the longitudinal and transverse axes, with the longitudinal axis exhibiting more uneven texture information. The polarization-degree images of the three disease spores exhibit variations in their representation within the entirety of the beam information. The disease spore polarization angle image exhibited the maximum levels of contrast and entropy when the Gabor filter’s direction was set to π/15. The recognition accuracy of cucumber downy mildew spores, tomato gray mildew spores, and cucumber powdery mildew spores were 75.00%, 83.33%, and 96.67%, respectively. The average recognition accuracy of disease spores was 86.67% based on BPNN and micropolarization image features. This study can provide a novel method for the detection of plant disease spores in the greenhouse.

关键词： greenhouse spores micropolarization image BPNN image processing detection

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Design of a Multi-sensor Monitoring System for Additive Manufacturing Process

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Nanomanufacturing and Metrology 2020年第2期3卷 142-150页

作者： Xing Peng Lingbao Kong Yao Chen Zhenyong Shan Lizhe Qi Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing Fudan UniversityShanghai 200433China ASAGE ROBOTS(Zhuhai)Co. LtdZhuhai 519085GuangdongChina Academy for Engineering and technology Fudan UniversityShanghai 200433China

Additive manufacturing(AM)technology is becoming increasingly feasible in industrial *** AM systems continue to improve,the lack of repeatability and stability of these technologies remains an obstacle to industrial ***,AM process monitoring equipment has to be set up to supervise product quality and detect *** study first reviews the various categories of defects,relevant AM process signatures,and optical monitoring methods of powder bed fusion processes proposed so ***,according to the detection requirements,an optical process monitoring system based on multiple sensors is proposed,which is equipped with a white light imaging channel,an infrared imaging channel,and a polarization imaging *** aberrations can be optimized using Zemax software by appropriately selecting the optimization function ***,the design requirements for the multi-sensor monitoring system for the AM process can be achieved.

关键词： Optical design Process monitoring Additive manufacturing Multi-sensor Zemax

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Data-driven polarimetric imaging: a review

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Opto-Electronic Science 2024年第2期3卷 1-44页

作者： Kui Yang Fei Liu Shiyang Liang Meng Xiang Pingli Han Jinpeng Liu Xue Dong Yi Wei Bingjian Wang Koichi Shimizu Xiaopeng Shao School of Optoelectronic Engineering Xidian UniversityXi’an 710071China School of Physics Xidian UniversityXi’an 710071China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu 808-0135Japan Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Hangzhou Institute of technology Xidian UniversityHangzhou 311200China

This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical *** widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and *** information is increasingly incorporated into convolutional neural networks(CNN)as a supplemental feature of objects to improve performance in computer vision task *** imaging and deep learning can extract abundant information to address various ***,this article briefly reviews recent developments in data-driven polarimetric imaging,including polarimetric descattering,3D imaging,reflection removal,target detection,and biomedical ***,we synthetically analyze the input,datasets,and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and *** also highlight the significance of data-driven polarimetric imaging in future research and development.

关键词： deep learning polarimetric imaging image processing

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Comparative study of differential polarization imaging using linear and circular polarization in different scattering medium

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Journal of Measurement Science and Instrumentation 2022年第2期13卷 173-183页

作者： TIAN Heng WU Yelin TIAN Jingjing ZHANG Bo ZHU Jingping School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo 454003 China Shaanxi key Lab of Information Photonic Technique Xi’an Jiaotong University Xi’an 710049 China

Differential polarization imaging has been widely used to selectively probe the target embedded in turbid medium.A thorough understanding of image quality involved in differential polarization imaging is essential for practical *** polarized light Monte Carlo simulations,it has been investigated how the state of polarization of incident light and the optical properties of scattering medium affect the image *** contrast for linear polarization is similar to that for circular polarization in the isotropic medium comprising *** image quality is more pronounced for circular polarization in the isotropic medium containing large-particles and the birefringent ***,differential polarization imaging provides better image quality for the birefringent medium compared with isotropic *** effect of particle-size and birefringence on the polarization characteristics of target light and backscattered light is *** the help of numerical results,the polarization characteristics of target light and backscattered light,the image quality is well explained in the turbid medium mentioned above.

关键词： polarization imaging target detection Monte Carlo simulation scattering birefringence

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Structured-illumination reflectance imaging for the evaluation of microorganism contamination in pork: Effects of spectral and imaging features on its prediction performance

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Food Science and Human Wellness 2024年

作者： Binjing Zhou Xiaohua Liu Yan Ge Kang Tu Jing Peng Juan Francisco García-Martín Jie Wu Weijie Lan Leiqing Pan College of Food Science and technology， Nanjing Agricultural University College of Engineering， Nanjing Agricultural University The Academy of Science， Nanjing Agricultural University Departamento de Ingeniería Química， Facultad de Química， Universidad de Sevilla School of Food and Biological Engineering， Bengbu University

Structured-illumination reflectance imaging provides a new means for food quality detection. This original work investigated the capability of Structured-illumination reflectance imaging technique coupled with multivariate chemometrics to evaluate the microbial contamination in pork inoculated with Pseudomonas fluorescens and Brochothrix thermosphacta during storage at different temperatures. The prediction performances based on different spectrum and the textural features of direct component and amplitude component images demodulated from the SIRI pattern images， as well as their data fusion were comprehensively compared. Based on the full wavelength spectrum (420 – 700) nm of amplitude component images， the orthogonal signal correction coupled with support vector machine regression provided the best predictions of the number of Pseudomonas fluorescens and Brochothrix thermosphacta in pork， with the Rp2values of 0.870 and 0.906， respectively. Besides， the prediction models based on the amplitude component or direct component image textural features and the data fusion models using spectrum and textural features from direct component and amplitude component images cannot significantly improve their prediction accuracy. Consequently， structured-illumination reflectance imaging can be further considered as a potential technique for the rapid evaluation of microbial contaminations in pork meat.

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Turbidity analysis using visible and near-infrared light images

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Journal of Measurement Science and Instrumentation 2021年第1期12卷 27-35页

作者： ZHU Yuanyang ZHAO Wenzhu LIU Sheng GAO Hongwen School of Computer Science and technology Huaibei Normal University Huaibei 235000 China College of Environmental Science and Engineering Tongji University Shanghai 200092 China

The conventional photoelectric detection system requires complex circuitry and spectroscopic systems as well as specialized personnel for its *** replace such a system,a method of measuring turbidity using a camera is proposed by combining the imaging characteristics of a digital camera and the high-speed information processing capability of a *** turbidity measurement devices based on visible and near-infrared(NIR)light cameras and a light source driving circuit with constant light intensity were *** RGB data in the turbidity images were acquired using a self-developed image processing software and converted to the CIE Lab color *** on the relationship between the luminance,chromatic aberration,and turbidity,the turbidity detection models for luminance and chromatic aberration of visible and NIR light devices exhibiting values from 0-1000 NTU,less than 100 NTU,and more than 100 NTU were *** comparing and analyzing the proposed models,the two measurement models with the best all-around performance were selected and fused to generate new measurement *** experimental results prove that the correlation between the three models and the commercial turbidity meter measurements exhibite a significance value higher than *** error of the fusion model is within 1.05%,with a mean square error of *** visible light device has less error at low turbidity measurements and is less influenced by the color of the *** NIR light device is more stable and accurate at full range and high turbidity measurements and is therefore more suitable for such measurements.

关键词： image processing water quality turbidity measurement near-infrared image color space conversion

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