Abstract
Tomato spotted wilt virus is a wide-spread plant disease in the world. It can threaten thousands of plants with a persistent and propagative manner. Early disease detection is expected to be able to control the disease spread, to facilitate management practice, and further to guarantee accompanying economic benefits. Hyperspectral imaging, a powerful remote sensing tool, has been widely applied in different science fields, especially in plant science domain. Rich spectral information makes disease detection possible before visible disease symptoms showing up. In the paper, a new hyperspectral analysis proximal sensing method based on generative adversarial nets (GAN) is proposed, named as outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). It is an all-in-one method, which integrates the tasks of plant segmentation, spectrum classification and image classification. The model focuses on image pixels, which can effectively visualize potential plant disease positions, and keep experts’ attention on these diseased pixels. Meanwhile, this new model can improve the performances of classic spectrum band selection methods, including the maximum variance principle component analysis (MVPCA), fast density-peak-based clustering, and similarity-based unsupervised band selection. Selecting spectrum wavebands reasonably is an important preprocessing step in spectroscopy/hyperspectral analysis applications, which can reduce the computation time for potential in-field applications, affect the prediction results and make the hyperspectral analysis results explainable. In the experiment, the hyperspectral reflectance imaging system covers the spectral range from 395 nm to 1005 nm. The proprosed model makes use of 83 bands to do the analysis. The plant level classification accuracy gets 96.25% before visible symptoms shows up. The pixel prediction false positive rate in healthy plants gets as low as 1.47%. Combining the OR-AC-GAN with three existing band selection algorithms, the performance of these band selection models can be significantly improved. Among them, MVPCA can leverage only 8 spectrum bands to get the same plant level classification accuracy as OR-AC-GAN, and the pixel prediction false positive rate in healthy plants is 1.57%, which is also comparable to OR-AC-GAN. This new model can be potentially transferred to other plant diseases detection applications. Its property to boost the performance of existing band selection methods can also accelerate the in-field applications of hyperspectral imaging technology.
Highlights
Www.nature.com/scientificreports off an epidemic[3]
Compared to RGB color imaging system, Hyperspectral imaging (HSI) including near infrared information makes early plant disease detection possible, and the subtle changes in spectral reflectance of plants could reflect early and invisible symptoms of the disease[12]
For the early stage plant disease detection application, it is very difficult to quantify pixel-level classification performance of invisible diseased pixels, and unsupervised band selection is more promising in this specific domain
Summary
Www.nature.com/scientificreports off an epidemic[3]. Since 2004, TSWV isolates have overcome the resistance gene Tsw in pepper[4], making it more difficult to manage. For the early stage plant disease detection application, it is very difficult to quantify pixel-level classification performance of invisible diseased pixels, and unsupervised band selection is more promising in this specific domain. The novel method can detect the TSWV disease sensitively at an early stage before the symptom is visible making use of full hyperspectral information. It can be potentially used in field for real-time applications because the generated data from the model can boost the performance of existing band selection algorithms and preserve the classification accuracy with limited bands
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