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computer vision based precision agriculture tasks. In European conference on computer vision
(pp. 105–116). Cham: Springer International Publishing.
Hao, P., Di, L., Zhang, C., & Guo, L. (2020). Transfer Learning for Crop classification with
Cropland Data Layer data (CDL) as training samples. Science of The Total Environment, 733,
138869.
Heinrich, G. (2016). https://devblogs.nvidia.com/parallelforall/image-segmentation-using-digits-5/
Hubel, D. H., & Wiesel, T. N. (1959). Receptive fields of single Neurones in the cat’s striate cortex.
The Journal of Physiology, 148(3), 574–591.
Jensen, J. R. (2015). Introductory digital image processing: A remote sensing perspective. In
Pearson series in geographic information science. Glenview: Pearson Education, Inc.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., . . ., & Darrell, T. (2014,
November). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the
22nd ACM international conference on multimedia (pp. 675–678). New York: ACM.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and pros. Science,
349(6245), 255–260.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep
convolutional neural networks. In Advances in neural information processing systems
(pp. 1097–1105). Berlin/New York: Springer.
Kuwata, K., & Shibasaki, R. (2015, July). Estimating crop yields with deep learning and remotely
sensed data. In 2015 IEEE International Geoscience and Remote Sensing Symposium
(IGARSS) (pp. 858–861). IEEE.
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META
Group Research Note, 6(70), 70–73.
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D.
(1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1
(4), 541–551.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.
Lee, S. H., Chan, C. S., Wilkin, P., & Remagnino, P. (2015, September). Deep-plant: Plant
identification with convolutional neural networks. In 2015 IEEE international conference on
image processing (ICIP) (pp. 452–456). IEEE.
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity.
The Bulletin of Mathematical Biophysics, 5(4), 115–133.
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant
disease detection. Frontiers in Plant Science, 7, 1419.
Montalvo, M., Guerrero, J. M., Romeo, J., Emmi, L., Guijarro, M., & Pajares, G. (2013). Automatic
expert system for weeds/crops identification in images from maize fields. Expert Systems with
Applications, 40(1), 75–82.
NASA. (2017). Earthdata system performance. https://earthdata.nasa.gov/about/system-perfor
mance. Accessed 01 Jan 2017.
Nativi, S., Mazzetti, P., Santoro, M., Papeschi, F., Craglia, M., & Ochiai, O. (2015). Big data
challenges in building the global earth observation system of systems. Environmental Modelling
& Software, 68, 1–26.
Ng, A. (2013). Courses – Andrew Ng. Retrieved from http://www.andrewng.org/courses/
Padarian, J., Minasny, B., & McBratney, A. B. (2015). Using Google’s cloud-based platform for
digital soil mapping. Computers & Geosciences, 83, 80–88.
Park, S., Im, J., Jang, E., & Rhee, J. (2016). Drought assessment and monitoring through blending
of multi-sensor indices using machine learning approaches for different climate regions. Agricultural and Forest Meteorology, 216, 157–169.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikitlearn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct),
2825–2830.
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.
100
C. Zhang and L. Lin