Page 251 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications
P. 251

242   Chapter 8 A review on plant diseases recognition through deep learning




                                    [30] J.R. Riley, Remote sensing in entomology, Annu. Rev. Entomol. 34 (1) (1989)
                                       247e271.
                                    [31] H. Nilsson, Remote sensing and image analysis in plant pathology, Annu.
                                       Rev. Phytopathol. 33 (1) (1995) 489e528.
                                    [32] M.P. Grisham, R.M. Johnson, P.V. Zimba, Detecting Sugarcane yellow leaf
                                       virus infection in asymptomatic leaves with hyperspectral remote sensing
                                       and associated leaf pigment changes, J. Virol Methods 167 (2) (2010)
                                       140e145.
                                    [33] X. Cao, Y. Luo, Y. Zhou, X. Duan, D. Cheng, Detection of powdery mildew
                                       in two winter wheat cultivars using canopy hyperspectral reflectance, Crop
                                       Protect. 45 (2013) 124e131.
                                    [34] D. Moshou, C. Bravo, J. West, S. Wahlen, A. McCartney, H. Ramon,
                                       Automatic detection of yellow rust in wheat using reflectance
                                       measurements and neural networks, Comput. Electron. Agric. 44 (2004)
                                       173e188.
                                    [35] J. Zhang, Y. Huang, L. Yuan, G. Yang, L. Chen, C. Zhao, Using satellite
                                       multispectral imagery for damage mapping of armyworm (Spodoptera
                                       frugiperda) in maize at a regional scale, Pest Manag. Sci. 72 (2015) 335e348.
                                    [36] T. Cheng, B. Rivard, G.A. S  anchez-Azofeifa, J. Feng, M. Calvo-Polanco,
                                       Continuous wavelet analysis for the detection of green attack damage due
                                       to mountain pine beetle infestation, Remote Sens. Environ. 114 (2010)
                                       899e910.
                                    [37] R. Calderón, J.A. Navas-Cort  es, C. Lucena, P.J. Zarco-Tejada, High-
                                       resolution airborne hyperspectral and thermal imagery for early detection
                                       of Verticillium wilt of olive using fluorescence, temperature and narrow-
                                       band spectral indices, Remote Sens. Environ. 139 (2013) 231e245.
                                    [38] H.R. Xu, Y.B. Ying, X.P. Fu, S.P. Zhu, Near-infrared spectroscopy in
                                       detecting leaf miner damage on tomato leaf, Biosyst. Eng. 96 (2007)
                                       447e454.
                                    [39] J. Zhang, R. Pu, L. Yuan, W. Huang, C. Nie, G. Yang, Integrating remotely
                                       sensed and meteorological observations to forecast wheat powdery mildew
                                       at a regional scale, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7 (2013)
                                       4328e4339.
                                    [40] N.R. Falkenberg, G. Piccinni, J.T. Cothren, D.I. Leskovar, C.M. Rush, Remote
                                       sensing of biotic and abiotic stress for irrigation management of cotton,
                                       Agric. Water Manag. 87 (1) (2007) 23e31.
                                    [41] A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep
                                       convolutional neural networks, in: Proceedings of the Advances in Neural
                                       Information Processing Systems, Lake Tahoe NV, USA, vol. 3e8, pp.
                                       1097e1105, 2012.
                                    [42] M. Sibiya, M. Sumbwanyambe, A computational procedure for the
                                       recognition and classification of maize leaf diseases out of healthy leaves
                                       using convolutional neural networks, Agric. Eng. 1 (2019) 119e131.
                                    [43] K. Zhang, Q. Wu, A. Liu, X. Meng, Can deep learning identify tomato leaf
                                       disease? Adv. Multimed. 10 (2018) 1e10.
                                    [44] J. Amara, B. Bouaziz, A. Algergawy, A deep learning-based approach for
                                       banana leaf diseases classification, in: Proceedings of the BTW (Workshops),
                                       Stuttgart, Germany, 6e10, pp. 79e88, 2017.
                                    [45] K.P. Ferentinos, Deep learning models for plant disease detection and
                                       diagnosis, Comput. Electron. Agric. 145 (2018) 311e318.
                                    [46] M. T€ urkogl.u, D. Hanbay, Plant disease and pest detection using deep
                                       learning-based features, Turk. J. Electr. Eng. Comput. Sci. 27 (3) (2019)
                                       1636e1651.
   246   247   248   249   250   251   252   253   254   255   256