Page 253 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

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

[64] J. Yue, W. Zhao, S. Mao, H. Liu, Spectralespatial classification of
hyperspectral images using deep convolutional neural networks, Remote
Sens. Lett. 6 (2015) 468e477.
[65] A. Signoroni, M. Savardi, A. Baronio, S. Benini, Deep learning meets
hyperspectral image analysis: a multidisciplinary review, J. Imaging 5 (2019)
52.
[66] X. Jin, L. Jie, S. Wang, H. Qi, S. Li, Classifying wheat hyperspectral pixels of
healthy heads and Fusarium head blight disease using a deep neural
network in the wild field, Remote Sens. 10 (2018) 395.
[67] D. Wang, R. Vinson, M. Holmes, G. Seibel, A. Bechar, S. Nof, Y. Tao, Early
detection of tomato spotted wilt virus by hyperspectral imaging and outlier
removal auxiliary classifier generative adversarial nets (OR-AC-GAN), Sci.
Rep. 9 (2019) 4377.
[68] K. Nagasubramanian, S. Jones, A.K. Singh, A. Singh,
B. Ganapathysubramanian, S. Sarkar, Explaining hyperspectral imaging
based plant disease identification: 3D CNN and saliency maps, arXiv 1804
(2018) 08831.
[69] X. Zhang, L. Han, Y. Dong, Y. Shi, W. Huang, L. Han, P. Gonz alez-Moreno,
H. Ma, H. Ye, T. Sobeih, Deep learning-based approach for automated
yellow rust disease detection from high-resolution hyperspectral UAV
images, Remote Sens. 11 (2019) 1554.