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.