Dr. Jianwei Qin Profile

Dr. Jianwei Qin

Research Agricultural Engineer at Agricultural Research Service

SPIE Involvement:

Conference Program Committee | Author

Publications (66)

SPIE Journal Paper | 8 February 2024 Open Access

Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers

Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Quentin Frederick, Megan M. Dewdney, Mark A. Ritenour

JARS, Vol. 18, Issue 01, 014512, (February 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.014512

KEYWORDS: Diseases and disorders, Education and training, Image classification, Reflectivity, Matrices, Classification systems, Principal component analysis, Hyperspectral imaging, Cameras, Machine learning

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Proceedings Article | 14 June 2023 Presentation

Federated learning for contamination detection and data privacy in food service industry (Conference Presentation)

Hamed Taheri Gorji, Mahdi Saeedi, Hossein Kashani Zadeh, Kaylee Husarik, Jianwei Qin, Diane Chan, Insuck Baek, Moon Kim, Alireza Akhbardeh, Stanislav Sokolov, Nicholas MacKinnon, Fartash Vasefi, Kouhyar Tavakolian

Proceedings Volume PC12545, PC1254505 (2023) https://doi.org/10.1117/12.2665343

KEYWORDS: Contamination, Industry, Data modeling, Luminescence, Evolutionary algorithms, Image segmentation, Safety, Pathogens, Instrument modeling, Imaging technologies

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Proceedings Article | 13 June 2023 Poster + Paper

Disinfection of Escherichia coli and Salmonella enterica using the contamination sanitization inspection and disinfection (CSI-D) device

J. McCoy Sanders, V. Alarcon, G. Marquis, A. Tabb, J. Van Kessel, J. Sonnier, B. Haley, I. Baek, J. Qin, M. Kim, F. Vasefi, S. Sokolov, R. Hellberg

Proceedings Volume 12545, 125450F (2023) https://doi.org/10.1117/12.2664049

KEYWORDS: Contamination, Inspection, Bacteria, Light emitting diodes, Ultraviolet radiation, Pathogens, Light sources and illumination, Diseases and disorders

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Proceedings Article | 13 June 2023 Presentation + Paper

Development of a multimodal sensing system for automated and intelligent food safety inspection

Jianwei Qin, Moon Kim, Jeehwa Hong, Hyunjeong Cho, Jo Ann Van Kessel, Insuck Baek, Kuanglin Chao

Proceedings Volume 12545, 1254504 (2023) https://doi.org/10.1117/12.2663710

KEYWORDS: Raman spectroscopy, Safety, Sensing systems, Inspection, Data modeling, Biological samples, Bacteria, Fluorescence, Spectrometers, Machine learning

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Proceedings Article | 13 June 2023 Presentation + Paper

Classifying E. coli concentration levels on multispectral fluorescence images with discrete wavelet transform, deep learning, and support vector machine

Pappu Kumar Yadav, Thomas Burks, Quentin Frederick, Jianwei Qin, Moon Kim, Mark Ritenour, Kunal Dudhe

Proceedings Volume 12545, 1254508 (2023) https://doi.org/10.1117/12.2663933

KEYWORDS: Education and training, Image classification, Discrete wavelet transforms, Deep learning, Machine learning, Fluorescence, Contamination, Wavelets, Lithium, Biofilms

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Escherichia coli (E. coli) bacteria when transmitted to humans by infected fruits like citrus can cause serious illnesses like bloody diarrhea and kidney disease (Hemolytic Uremic Syndrome). Therefore, inspection for the presence of E.coli colonies on fruits and vegetables would be beneficial for food safety if an appropriate sensor and detection approach were available. In this paper, we evaluate the efficacy of SafetySpect’s Contamination, Sanitization Inspection, and Disinfection (CSI-D+) system, which consists of an UV camera, an RGB camera, and illumination at two fluorescence excitation wavelengths, ultraviolet C (UVC) at 275 nm and violet at 405 nm, respectively. For this study, bacterial population at four different concentration levels were inoculated on black rubber slides. The black rubber slides provided a fluorescence-free background for benchmark tests on E.coli-containing droplets, since even fruit peels have a subdued fluorescence response which appears as low intensity noise in the background of the UV images. To increase the number of image datasets to avoid overfitting issue of deep leaning and machine learning models, synthetic datasets were generated using StyleGAN2- ADA generative network. As a first approach, the fluorescence images were denoised using discrete wavelet transform (DWT) and reconstructed denoised images were used to train StyleGAN2-ADA for generating larger datasets which were later used by VGG19 with SoftMax classifier. In the second approach, VGG19 was used to extract features and then Linear and RBF SVM were used for classification. It was found that using Symlets family of DWT denoised images at an average PSNR value of 25.59 dB. VGG19 with SoftMax was able to classify the images at overall accuracy of 84% without synthetic dataset and at 94% by using augmented dataset generated by StyleGAN2-ADA. It was also found that using RBF SVM increased the accuracy by 2% to 96% and linear SVM by 3% to 97%. The findings reported here could be used to detect presence of E. coli bacterial population on citrus peels for taking the necessary actions for decontamination.

Proceedings Article | 13 June 2023 Presentation + Paper

Cleanliness assessment in long-term care facilities using deep learning and multiwavelength fluorescence imaging

Kaylee Husarik, Hamed Taheri Gorji, Jianwei Qin, Diane Chan, Insuck Baek, Moon Kim, Mona Sohrabi Thompson, Nicholas MacKinnon, Stanislav Sokolov, Fartash Vasefi, Michael Johnson, Melanie Metz, Kouhyar Tavakolian

Proceedings Volume 12545, 1254507 (2023) https://doi.org/10.1117/12.2663393

KEYWORDS: Contamination, Fluorescence imaging, Fluorescence, Light sources and illumination, Cameras, Imaging systems, Optical testing, Deep learning, Acquisition tracking and pointing, Medicine

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Proceedings Article | 13 June 2023 Paper

Dual-excitation fluorescence imaging system for contamination detection in food facilities

Connor Propp, Luke Woods, Hamed Taheri Gorji, Kaylee Husarik, Mitchell Sueker, Jianwei Qin, Insuck Baek, Moon Kim, Diane Chan, Stanislav Sokolov, Nicholas MacKinnon, Fartash Vasefi, Kouhyar Tavakolian

Proceedings Volume 12545, 125450G (2023) https://doi.org/10.1117/12.2665364

KEYWORDS: Contamination, Imaging systems, Fluorescence, Fluorescence imaging, Cameras, Light sources and illumination, Emission wavelengths, Ultraviolet radiation, Chlorophyll, Acquisition tracking and pointing

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Proceedings Article | 13 June 2023 Paper

Efficacy of sanitization in healthcare using deep learning and multiwavelength fluorescence imaging

Luke Woods, Connor Propp, Mitch Sueker, Kaylee Husarik, Hamed Taheri Gorji, Jianwei Qin, Insuck Baek, Moon Kim, Diane Chan, Stanislav Sokolov, Nicholas MacKinnon, Fartash Vasefi, Kouhyar Tavakolian

Proceedings Volume 12545, 125450H (2023) https://doi.org/10.1117/12.2665702

KEYWORDS: Medicine, Contamination, Cameras, Imaging systems, Fluorescence, Fluorescence imaging, Inspection, Ultraviolet radiation, Light emitting diodes, Telecommunications

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Proceedings Article | 13 June 2023 Poster + Paper

Raman enhancement effect of different Au @ Ag NPs on S. Typhi

Yahui Chen, Yankun Peng, Qinghui Guo, Jianwei Qin

Proceedings Volume 12545, 125450C (2023) https://doi.org/10.1117/12.2663272

KEYWORDS: Silver, Gold, Raman spectroscopy, Surface enhanced Raman spectroscopy, Bacteria, Nanoparticles, Pathogens, Colloids, Raman scattering, Gold nanoparticles

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Proceedings Article | 13 June 2023 Presentation + Paper

Citrus disease classification with convolution neural network generated features and machine learning classifiers on hyperspectral image data

Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Quentin Frederick, Megan Dewdney, Mark Ritenour

Proceedings Volume 12539, 1253902 (2023) https://doi.org/10.1117/12.2665768

KEYWORDS: Diseases and disorders, Image classification, Reflectivity, Principal component analysis, Convolution, Classification systems, Hyperspectral imaging, Imaging systems, Deep learning, Hyperspectral systems

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Citrus black spot (CBS) is a quarantine fungal disease caused by Phyllosticta citricarpa that can limit market access for fruit. It causes lesions on fruit surfaces and may lead to premature fruit drops, reducing yield. Leaf symptoms are uncommon for CBS, although the fungus reproduces in leaf litter. Similarly, citrus canker is another serious disease caused by the bacterium Xanthomonas citri subsp. citri (syn. X. axonopodis pv. citri) and leads to economic losses for growers from fruit drops and blemishes. Therefore, early detection and management of groves infected by CBS or canker via fruit and/or leaf inspection can benefit the Florida citrus industry. Manual inspection to classify disease symptoms on either fruits or leaves is a tedious and labor intensive process. Hence, there is need to develop computer vision system for autonomous classification of fruits and leaves that can speed up their management in fields. In this paper, we demonstrate the capability of convolution neural network (CNN)-based deep learning along with classical machine learning (ML) based computer vision algorithms to classify ‘Valencia’ orange fruit surfaces with CBS infection along with four other conditions and ‘Furr’ mandarin leaves with canker and four other conditions. Fruits with CBS and four other conditions (marketable, greasy spot, melanose and wind scar) were classified using a custom shallow CNN with SoftMax and RBF SVM at an overall accuracy of 89.8% and 92.1%, respectively. Similarly, a custom VGG16 network with SoftMax could classify canker leaves with F1-score of 85% and overall accuracy of 82% including other four conditions (control/healthy, greasy spot, melanose and scab). In addition, it was found that by replacing SoftMax with RBF SVM in the VGG16 network, the overall classification accuracy improved to 93% i.e., an increment of 11% points (13.41%). The preliminary findings reported in this paper demonstrate the capability of HSI system for automated citrus fruit and leaf disease classification using shallow and deep CNN generated features and ML classifiers.

Proceedings Article | 1 June 2022 Presentation + Paper

Development of a hyperspectral imaging system for plant health monitoring in space crop production

Jianwei Qin, Oscar Monje, Matthew Nugent, Joshua Finn, Aubrie O’Rourke, Ralph Fritsche, Insuck Baek, Diane Chan, Moon Kim

Proceedings Volume 12120, 1212004 (2022) https://doi.org/10.1117/12.2618635

KEYWORDS: Reflectivity, Imaging systems, Luminescence, Hyperspectral imaging, Cameras, Space operations, RGB color model, Line scan image sensors, Light emitting diodes, Software development

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Proceedings Article | 1 June 2022 Presentation + Paper

Identification of aflatoxin contamination in corn kernels using line-scan Raman imaging

Feifei Tao, Haibo Yao, Zuzana Hruska, Kanniah Rajasekaran, Jianwei Qin, Moon Kim

Proceedings Volume 12120, 1212002 (2022) https://doi.org/10.1117/12.2624965

KEYWORDS: Raman spectroscopy, Contamination, Line scan image sensors, Chemical analysis, Agriculture, Statistical modeling, Imaging systems, Hyperspectral imaging

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