1. Y. Zheng, Q. Zhu, M. Huang, Y. Guo, J. Qin, Maize and weed classification using color indices with support vector data description in outdoor fields, Comput. Electron. Agric., 141 (2017) 215–222.
  2. S. Shahbudin, A. Hussain, S.A. Samad, M.M. Mustafa, A.J. Ishak, Optimal Feature Selection for SVM Based Weed Classification via Visual Analysis, IEEE Region 10 Annual International Conference, 1 (2010) 1647–1650.
  3. I. Sa, Z. Chen, M. Popovic, R. Khanna, F. Liebisch, J. Nieto, R. Siegwart, WeedNet: dense semantic weed classification using multispectral images and MAV for smart farming, IEEE Rob. Autom. Lett., 3 (2018) 588–595.
  4. I. Ahmad, M.H. Siddiqi, I. Fatima, Weed Classification Based on Haar Wavelet Transform via K-nearest Neighbor (k-NN) for Real-time Automatic Sprayer Control System, International Conference on Ubiquitous Information Management and Communication, 1 (2017) 17.
  5. F. Vesali, M. Gharibkhani, M.H. Komarizadeh, Performance evaluation of discriminant analysis and decision tree, for weed classification of potato fields, Res. J. Appl. Sci. Eng. Technol., 4 (2012) 3215–3221.
  6. S. Lavania, P.S. Matey, Novel Method for Weed Classification in Maize Field Using Otsu and PCA Implementation, IEEE International Conference on Computational Intelligence and Communication Technology, 1 (2015) 534–537.
  7. S. Zhi, Y. Liu, X. Li, Y. Guo, Toward real-time 3D object recognition: A lightweight volumetric CNN framework using multitask learning, Comput. Graphics (Pergamon), 71 (2018) 199–207.
  8. K. Yanai, R. Tanno, K. Okamoto, Efficient Mobile Implementation of A CNN-based Object Recognition System, MM 2016, Proc. 2016 ACM Multimedia Conference, 1 (2016) 362–366.
  9. A. Qayyum, A.S. Malik, N.M. Saad, M. Iqbal, M.F. Abdullah, W. Rasheed, T. AB Rashid Abdullah, M.Y.B. Jafaar, Scene classification for aerial images based on CNN using sparse coding technique, Int. J. Remote Sens., 38 (2017) 2662–2685.
  10. Y. Zhao, J. Ma, X. Li, J. Zhang, Saliency detection and deep learning-based wildfire identification in UAV imagery, Sensors, 18 (2018) 712.
  11. W. Li, H. Fu, L. Yu, A. Cracknell, Deep learning based oil palm tree detection and counting for high-resolution remote sensing images, Remote Sens., 9 (2017) 22.
  12. J.S. Moon, C. Kim, Y. Youm, J. Bae, UNI-Copter: A portable single-rotor-powered spherical unmanned aerial vehicle (UAV) with an easy-to-assemble and flexible structure, J. Mech. Sci. Technol., 32 (2018) 2289–2298.
  13. X. Qi, J. Qi, D. Theilliol, D. Song, Y. Zhang, J. Han, Selfhealing control design under actuator fault occurrence on single-rotor unmanned helicopters, J. Intell. Rob. Syst., 84 (2016) 21–35.
  14. X. Zhang, X. Li, H. Pei, R. Huang, Design of self balancing anti disturbance system for multi rotor UAV, Telkomnika (Telecommunication Computing Electronics and Control), 14 (2016) 363–371.
  15. S. Wang, Z. Zhen, J. Jiang, X. Wang, Flight tests of autopilot integrated with fault-tolerant control of a small fixedwing UAV, Math. Prob. Eng., 2016 (2016), https://doi. org/10.1155/2016/2141482.
  16. C. Li, J. Shen, S. Zhai, C. Wang, J. Yang, Active flow vector flight control using only SJAs for a fixed-wing UAV, IEEE Access, 6 (2018) 76535–76545.
  17. A. Oscar, C.T. Calafate, Z.N. Roberto, N. Enrico, H.O. Enrique, C. Juan-Carlos, M. Pietro, A discretized approach to air pollution monitoring using UAV-based sensing, Mobile Network Appl., 23 (2018) 1693–1702.
  18. M. Shi, K. Qin, K. Li, Y. Zheng, Design and testing on autonomous multi-UAV cooperation for high-voltage transmission line inspection, Autom. Electr. Power Syst., 41 (2017) 117–122.
  19. C. Nived, L. Thomas, S. Cyrill, Robust long-term registration of UAV images of crop fields for precision agriculture, IEEE Rob. Autom. Lett., 3 (2018) 3097–3104.
  20. D. Yin, L. Wang, Individual mangrove tree measurement using UAV-based LiDAR data: possibilities and challenges, Remote Sens. Environ., 223 (2019) 34–49.
  21. B. Mesay Belete, Z. Abdallah, N. Abdelhamid, M. Farid, A convolutional neural network approach for assisting avalanche search and rescue operations with UAV imagery, Remote Sens., 9 (2017) 100,
  22. L.F. Gonzalez, G.A. Montes, E. Puig, S. Johnson, K. Mengersen, K.J. Gaston, Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation, Sensors, 16 (2016) 97–115.
  23. X. Wang, H. Sun, Y. Long, L. Zheng, H. Liu, M. Li, Development of visualization system for agricultural UAV crop growth information collection, IFAC-Papers On Line, 51 (2018) 631–636.
  24. Z. Wei, Y. Han, M. Li, K. Yang, Y. Yang, Y. Luo, S. Ong, A small UAV based multi-temporal image registration for dynamic agricultural terrace monitoring, Remote Sens., 9 (2017) 904,
  25. P. Zhang, K. Wang, Q. Lyu, S. He, S. Yi, R. Xie, Y. Zheng, Y. Ma, L. Deng, Droplet distribution and control against citrus Leafminer with UAV spraying, Int. J. Rob. Autom., 32 (2017) 299–307.
  26. D.H. Hubel, T.N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex, J. Physiol., 160 (1962) 106–154.
  27. S. Miyake, K. Fukushima, A neural network model for the mechanism of feature-extraction, Biol. Cybern., 50 (1984) 377–384.
  28. Y. Lecun, B. Boser, J.S. Denker, Backpropagation applied to handwritten zip code recognition, Neural Comput., 1 (2014) 541–551.
  29. Y. Lecun, L. Bottou, Y. Bengio, Gradient-based learning applied to document recognition, Proc. IEEE, 86 (1998) 2278–2324.
  30. K. Alex, I. Sutskever, G. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS Curran Associates Inc., 2012.
  31. A. Radman, N. Zainal, S.A. Suandi, Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut, Digital Signal Process, 64 (2017) 60–70.