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Wang, D., University of Maryland, Bio-Imaging and Machine Vision Laboratory, Fischell Department of Bioengineering, College Park, MD  20740, United States; Vinson, R., University of Maryland, Bio-Imaging and Machine Vision Laboratory, Fischell Department of Bioengineering, College Park, MD  20740, United States; Holmes, M., University of Maryland, Bio-Imaging and Machine Vision Laboratory, Fischell Department of Bioengineering, College Park, MD  20740, United States; Seibel, G., University of Maryland, Bio-Imaging and Machine Vision Laboratory, Fischell Department of Bioengineering, College Park, MD  20740, United States; USDA ARS, BLDG 002 BARC-WEST, 10300 Baltimore Ave, Beltsville, MD  20705, United States; Nof, S., School of Industrial Engineering, Purdue University, 315 N Grant Street, West Lafayette, IN  47907-2023, United States; Luo, Y., USDA ARS, BLDG 002 BARC-WEST, 10300 Baltimore Ave, Beltsville, MD  20705, United States; Tao, Y., University of Maryland, Bio-Imaging and Machine Vision Laboratory, Fischell Department of Bioengineering, College Park, MD  20740, United States

Hyperspectral imaging is a powerful technique in the agriculture field. The subtle changes in spectral reflectance of plants could reflect the invisible symptoms of plant diseases, which promises to be applied for detecting plant disease in the early stage. In practice, compared to the image-level classification, pixel-level (spectrum-level) classification could show tiny defects of plant, and keep experts’ attentions on the diseased pixels. However, in most cases, because there is no obvious visible difference between healthy spectrum and diseased spectrum, it is still an open research topic to conduct accurate pixel-level classification. Meanwhile, the outliers in the training dataset, the uncertainty of illumination conditions and the imbalance number of healthy pixels and diseased pixels in the training dataset, dramatically increase the difficulties of the problem. Targeting to these issues, based on a well-known deep leaning architecture, generative adversarial nets (GAN), a new deep learning architecture, outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN) is proposed. It not only integrates the classification task in the model, but also can weaken the side-effects of data outliers and find the intrinsic data features. In the experiment, a wide-spread disease Tomato Spotted Wilt Virus (TSWV) is used for validating the model. For the pixel-level classification, the average false positive rate of plant pixels in the healthy plants is 1.47%. For the plant-level classification, the corresponding sensitivity and specificity values are 92.59% and 100%. Compared to one dimensional convolutional neural network and auxiliary classifier GAN architecture, the proposed OR-AC-GAN model achieves the best results. In theory, the proposed model can be used for hyperspectral image analysis and early detection of any plant diseases. © 2018 American Society of Agricultural and Biological Engineers. All rights reserved.

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Early tomato spotted wilt virus detection using hyperspectral imaging

Wang, D., University of Maryland, Bio-Imaging and Machine Vision Laboratory, Fischell Department of Bioengineering, College Park, MD  20740, United States; Vinson, R., University of Maryland, Bio-Imaging and Machine Vision Laboratory, Fischell Department of Bioengineering, College Park, MD  20740, United States; Holmes, M., University of Maryland, Bio-Imaging and Machine Vision Laboratory, Fischell Department of Bioengineering, College Park, MD  20740, United States; Seibel, G., University of Maryland, Bio-Imaging and Machine Vision Laboratory, Fischell Department of Bioengineering, College Park, MD  20740, United States; USDA ARS, BLDG 002 BARC-WEST, 10300 Baltimore Ave, Beltsville, MD  20705, United States; Nof, S., School of Industrial Engineering, Purdue University, 315 N Grant Street, West Lafayette, IN  47907-2023, United States; Luo, Y., USDA ARS, BLDG 002 BARC-WEST, 10300 Baltimore Ave, Beltsville, MD  20705, United States; Tao, Y., University of Maryland, Bio-Imaging and Machine Vision Laboratory, Fischell Department of Bioengineering, College Park, MD  20740, United States

Early tomato spotted wilt virus detection using hyperspectral imaging

Hyperspectral imaging is a powerful technique in the agriculture field. The subtle changes in spectral reflectance of plants could reflect the invisible symptoms of plant diseases, which promises to be applied for detecting plant disease in the early stage. In practice, compared to the image-level classification, pixel-level (spectrum-level) classification could show tiny defects of plant, and keep experts’ attentions on the diseased pixels. However, in most cases, because there is no obvious visible difference between healthy spectrum and diseased spectrum, it is still an open research topic to conduct accurate pixel-level classification. Meanwhile, the outliers in the training dataset, the uncertainty of illumination conditions and the imbalance number of healthy pixels and diseased pixels in the training dataset, dramatically increase the difficulties of the problem. Targeting to these issues, based on a well-known deep leaning architecture, generative adversarial nets (GAN), a new deep learning architecture, outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN) is proposed. It not only integrates the classification task in the model, but also can weaken the side-effects of data outliers and find the intrinsic data features. In the experiment, a wide-spread disease Tomato Spotted Wilt Virus (TSWV) is used for validating the model. For the pixel-level classification, the average false positive rate of plant pixels in the healthy plants is 1.47%. For the plant-level classification, the corresponding sensitivity and specificity values are 92.59% and 100%. Compared to one dimensional convolutional neural network and auxiliary classifier GAN architecture, the proposed OR-AC-GAN model achieves the best results. In theory, the proposed model can be used for hyperspectral image analysis and early detection of any plant diseases. © 2018 American Society of Agricultural and Biological Engineers. All rights reserved.

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