observation data from a variety of imaging sensors is becoming a new challenge in
agro-geoinformatics. We covered several traditional and state-of-the-art methods,
including knowledge-based expert system, machine learning-based decision, and
artificial neural network, for agro-geoinformation extraction from image data. As a
case study of image processing in the agricultural observation system, we introduced
the CDL program of USDA NASS as well as the image processing methods for the
production of CDL data. In the future, we will explore more image processing
methods and applications in agro-geoinformatics. Meanwhile, the next-generation
technologies of image processing in the agricultural observation system, such as the
artificial intelligence-enabled agro-geoinformation extraction, will be systematically
investigated.
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., et al. (2016). Tensorflow:
Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv,
1603.04467.
Akerkar, R., & Sajja, P. (2010). Knowledge-based systems. Sudbury, MA: Jones & Bartlett
Publishers.
Barker, J., & Prasanna, S. (2016). https://devblogs.nvidia.com/parallelforall/deep-learning-objectdetection-digits/
Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: The US
department of agriculture, national agricultural statistics service, cropland data layer program.
Geocarto International, 26(5), 341–358.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression
trees. Boca Raton: CRC Press.
Ciregan, D., Meier, U., & Schmidhuber, J. (2012, June). Multi-column deep neural networks for
image classification. In Computer vision and pattern recognition (CVPR), 2012 IEEE conference on (pp. 3642–3649). IEEE.
Collobert, R., Kavukcuoglu, K., & Farabet, C. (2011). Torch7: A matlab-like environment for
machine learning. In BigLearn, NIPS workshop (No. EPFL-CONF-192376).
Copeland, M. (2016). The difference between AI, machine learning, and deep learning? | NVIDIA
Blog. Retrieved June 01, 2017, from https://blogs.nvidia.com/blog/2016/07/29/whatsdifference-artificial-intelligence-machine-learning-deep-learning-ai/.
Fukushima, K., & Miyake, S. (1982). Neocognitron: A self-organizing neural network model for a
mechanism of visual pattern recognition. In Competition and cooperation in neural nets
(pp. 267–285). Berlin/Heidelberg: Springer.
Gao, J. (2008). Digital analysis of remotely sensed imagery. New York: McGraw-Hill Professional.
Gonzalez, R. C., Woods, R. E. (2008). Digital image processing, 3rd Edition. Upper Saddle River,
NJ: Pearson.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google
Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment,
202, 18–27.
Han, W., Yang, Z., Di, L., & Mueller, R. (2012). CropScape: A web service based application for
exploring and disseminating US conterminous geospatial cropland data products for decision
support. Computers and Electronics in Agriculture, 84, 111–123.
6 Image Processing Methods in Agricultural Observation Systems
99
agro-geoinformatics. We covered several traditional and state-of-the-art methods,
including knowledge-based expert system, machine learning-based decision, and
artificial neural network, for agro-geoinformation extraction from image data. As a
case study of image processing in the agricultural observation system, we introduced
the CDL program of USDA NASS as well as the image processing methods for the
production of CDL data. In the future, we will explore more image processing
methods and applications in agro-geoinformatics. Meanwhile, the next-generation
technologies of image processing in the agricultural observation system, such as the
artificial intelligence-enabled agro-geoinformation extraction, will be systematically
investigated.
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., et al. (2016). Tensorflow:
Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv,
1603.04467.
Akerkar, R., & Sajja, P. (2010). Knowledge-based systems. Sudbury, MA: Jones & Bartlett
Publishers.
Barker, J., & Prasanna, S. (2016). https://devblogs.nvidia.com/parallelforall/deep-learning-objectdetection-digits/
Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: The US
department of agriculture, national agricultural statistics service, cropland data layer program.
Geocarto International, 26(5), 341–358.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression
trees. Boca Raton: CRC Press.
Ciregan, D., Meier, U., & Schmidhuber, J. (2012, June). Multi-column deep neural networks for
image classification. In Computer vision and pattern recognition (CVPR), 2012 IEEE conference on (pp. 3642–3649). IEEE.
Collobert, R., Kavukcuoglu, K., & Farabet, C. (2011). Torch7: A matlab-like environment for
machine learning. In BigLearn, NIPS workshop (No. EPFL-CONF-192376).
Copeland, M. (2016). The difference between AI, machine learning, and deep learning? | NVIDIA
Blog. Retrieved June 01, 2017, from https://blogs.nvidia.com/blog/2016/07/29/whatsdifference-artificial-intelligence-machine-learning-deep-learning-ai/.
Fukushima, K., & Miyake, S. (1982). Neocognitron: A self-organizing neural network model for a
mechanism of visual pattern recognition. In Competition and cooperation in neural nets
(pp. 267–285). Berlin/Heidelberg: Springer.
Gao, J. (2008). Digital analysis of remotely sensed imagery. New York: McGraw-Hill Professional.
Gonzalez, R. C., Woods, R. E. (2008). Digital image processing, 3rd Edition. Upper Saddle River,
NJ: Pearson.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google
Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment,
202, 18–27.
Han, W., Yang, Z., Di, L., & Mueller, R. (2012). CropScape: A web service based application for
exploring and disseminating US conterminous geospatial cropland data products for decision
support. Computers and Electronics in Agriculture, 84, 111–123.
6 Image Processing Methods in Agricultural Observation Systems
99