![]() Malthus TJ, Madeira AC (1993) High resolution spectroradiometry: spectral reflectance of field bean leaves infected by Botrytis fabae. Mahlein A-K (2016) Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Lowe A, Harrison N, French AP (2017) Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Technical report, Department of Computer Science, National Taiwan University Lin HT and Lin CJ (2003) A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Larsolle A, Muhammed HH (2007) Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Kobayashi T, Kanda E, Kitada K, Ishiguro K, Torigoe Y (2001) Detection of Rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Knipling EB (1970) Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Knauer U, Matros A, Petrovic T, Zanker T, Scott ES, Seiffert U (2017) Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images. Keerthi SS, Lin CJ (2003) Asymptotic behaviors of support vector machines with Gaussian kernel. Hsu CW, Chang CC, Lin CJ (2016) A Practical Guide to Support Vector Classication. Goetz AFH, Vane G, Solomon JE, Rock BN (1985) Imaging spectrometry for earth remote sensing. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China (2009) Rules of investigation and foreast of the rice blast. Proceedings of 4th EARSeL Workshop on Imaging Spectroscopy pp:677–689ĭevadas D, Lamb DW, Simpfendorfer S, Backhouse D (2009) Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Mach Learn 20:273–297ĭelalieux S, Aardt JV, Keulemans W, Coppin P (2005) Detection of Biotic Stress ( Venturia inaequalis) in Apple Trees Using Hyperspectral Analysis. ![]() ![]() Phytopathology 93:1524–1532Ĭortes C, Vapnik V (1995) Support-vector networks. & life Sci.) 37(3):307-311Ĭoops N, Stanford M, Old K, Dudzinski M, Culvenor D, Stone C (2003) Assessment of Dothistroma needle blight of Pinus radiata using airborne hyperspectral imagery. Acm TIST 2(27):1–27Ĭheng SX, Shao YN, Wu D, He Y (2011) Determination of rice leaf blast disease level based on visible-near-infrared spectroscopy. Crit rev plant Sci 2(29):59–107Ĭhang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. Infections on Wheat Agriculture 4:32–57īock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by Hyperspectral imaging. Comput Electron Agr 75:304–312īauriegel E, Herppich WB (2014) Hyperspectral and chlorophyll fluorescence imaging for early detection of plant diseases, with special reference to Fusarium spec. Physiol Mol Plant Pathol 108:101426īauriegel E, Giebel A, Geyer M, Schmidt U, Herppich WB (2011) Early detection of Fusarium infection in wheat using hyper-spectral imaging. According to our results, the SRR data reconstruction method presented here can be used to assess rice leaf blast severity during late vegetative growth.Īli MM, Bachik NA, Atirah Muhadi N, Yusof TNT, Gomes C (2019) Non-destructive techniques of detecting plant diseases: a review. ![]() ![]() The classification accuracy of the model at jointing, booting and heading stages was 83.33%, 97.06% and 83.87%, respectively. A support vector machine model was constructed to identify five infection severities based on the transformed data. To analyze the hyperspectral data, we presented a spectral reflectance ratio (SRR) data reconstruction method. We obtained hyperspectral images of rice leaves and extracted average spectral reflectance data for entire leaves and undiseased leaf regions. To carry out the study under natural conditions, rice was cultivated without any disease control measures. Experiments were carried out on Mongolian rice, which is susceptible to the disease. The objective of this study was to present and evaluate a data reconstruction method for assessment of rice leaf blast severity using hyperspectral imaging technology at the late vegetative growth stage. Rice blast ( Magnaporthe grisea) is an epidemic rice disease that reduces rice yield and quality worldwide. ![]()
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