Abstract
Identifying which crop is growing in certain areas is important to many national and multinational agricultural agencies for forecasting grain supplies, monitoring farming activity, facilitating crop rotation records, etc. In order to achieve that, the agencies require to schedule censuses on a regular basis. Recently, different techniques based on remote sensing have been applied to collect the information and perform a crop classification task. In this paper, we described a methodology to perform a crop classification task based on the Gray Level Co-Occurrence Matrix (GLCM) and the artificial bee colony (ABC) algorithm. The proposed methodology selects the set of features from the GLCM that allow classify the crops with a good accuracy using the ABC algorithm in terms of a distance classifier. The accuracy of the proposed methodology was tested over a specific region of Mexico and compared against different distance classifiers.
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Acknowledgments
The authors thank DGAPA, UNAM and Universidad La Salle for the economic support under grants number I-61/12 and NEC-03/15. Beatriz Garro thanks CONACYT for the posdoctoral scholarship.
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Vazquez, R.A., Garro, B.A. (2016). Crop Classification Using Artificial Bee Colony (ABC) Algorithm. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_18
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DOI: https://doi.org/10.1007/978-3-319-41009-8_18
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