Identification of Oil-Palm Plantation for Agricultural Survey based on Texture Analysis and Crowdsource Data

Supattra Puttinaovarat, Paramate Horkaew, Thanyathep Saengchot, Sudarat Sinwisarn

Abstract


This paper focuses on identification of oil-palm plantation areas from Thaichote satellite imagery based on texture analysis. The resultant extraction would then be merged with crowdsource data for agricultural survey. The experiments reported herein exhibited a reasonably high averaged accuracy, precession, recall and Kappa of 94.87% 82.44% 93.90% and 0.85, respectively. The mentioned processing module was implemented on a web application with responsive user interface design, which supported a wide range of devices with various resolutions. Based on available device’s positioning system, oil-palm plantation pin points were effectively acquired from user own locations, which enabling crowdsource updates on actual plantation. The computationally extracted areas by texture analysis could be up- and downloaded and then fused with farmers’ input ones, for validation and hence information integrity. Furthermore, various attributes related to or has any effect on oil-palm plantation could also be communicated and stored for subsequent analyses and preparation of relevant reports. The proposed system therefore could greatly benefit agricultural survey and acquisition of oil-palm plantations irrespective of terrestrial and temporal accessibility. Accordingly, the system tremendously reduces cost and time required for onsite survey, while offering reliable and real-time data for various agricultural and other purposes.

 

Keywords :  oil-Palm plantation areas identification, oil-palm plantation areas classification, crowdsource data, texture analysis, Gabor filter


Full Text:

PDF

References


Agustin, S., Ginardi, R. H., & Tjandrasa, H. (2015). Identification of oil palm plantation in IKONOS images using radially averaged power spectrum values. In Proceeding Information & Communication Technology and Systems (ICTS), 2015 International Conference (pp. 89-94). IEEE.

Daliman, S., Rahman, S. A., Bakar, S. A., & Busu, I. (2014). Segmentation of oil palm area based on GLCM-SVM and NDVI. In Proceeding Region 10 Symposium, 2014 IEEE (pp. 645-650). IEEE.

Fohringer, J., Dransch, D., Kreibich, H., & Schröter, K. (2015). Social media as an information source for rapid flood inundation mapping. Natural Hazards and Earth System Sciences, 15(12), 2725-2738.

Gabor, D. (1946). Theory of communication. Part 1: The analysis of information. Electrical Engineers-Part III: Radio and Communication Engineering, Journal of the Institution, 93(26), 429-441.

Gandharum, L., & Chen, C. F. (2011). USE OF FORMOSAT-2 SATELLITE IMAGERY TO CLASSIFY OIL PALM IN INDONESIA. (2011). In Proceeding 32nd Asian Conference on Remote Sensing 2011. (pp. 3-8).

Haghighat, M., Zonouz, S., & Abdel-Mottaleb, M. (2015). Cloud: Trustworthy cloud-based and cross-enterprise biometric identification. Expert Systems with Applications, 42(21), 7905-7916.

Haralick, R.M., Shanmugan, K., and Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, 3(6), 610-621.

Hardanto, A., Röll, A., Niu, F., Meijide, A., & Hölscher, D. (2017). Oil Palm and Rubber Tree Water Use Patterns: Effects of Topography and Flooding. Frontiers in plant science, 8, 452.

Horita, F. E. A., Degrossi, L. C., de Assis, L. F. G., Zipf, A., and de Albuquerque, J. P. (2013). The use of volunteered geographic information (VGI) and crowdsourcing in disaster management: a systematic literature review. Proceedings of the Nineteenth Americas Conference on Information Systems, (pp. 1-10).

Ministry of Agriculture and Cooperatives. (2015). Agri-Map-Online. Retrieved January 1, 2018, from http://agri-map-online.moac.go.th/static/file/agrimap-manual.pdf

Mirzapour, F., & Ghassemian, H. (2013). Using GLCM and Gabor filters for classification of PAN images. In Electrical Engineering (ICEE), 2013 21st Iranian Conference (pp. 1-6). IEEE.

Nagi, J., Ahmed, S. K., & Nagi, F. (2008). Palm biodiesel an alternative green renewable energy for the energy demands of the future. In International Conference on Construction and Building Technology, ICCBT

(pp. 79-94).

Okoro, S. U., Schickhoff, U., Böhner, J., & Schneider, U. A. (2016). A novel approach in monitoring land-cover change in the tropics: oil palm cultivation in the Niger Delta, Nigeria. DIE ERDE–Journal of the Geographical Society of Berlin, 147(1), 40-52.

Simon, T., Goldberg, A., and Adini, B. (2015). Socializing in emergencies—A review of the use of social media in emergency situations. International Journal of Information Management, 35(5), 609-619.

Srestasathiern, P., & Rakwatin, P. (2014). Oil palm tree detection with high resolution multi-spectral satellite imagery. Remote Sensing, 6(10), 9749-9774.

Umar, M. S., Jennings, P., & Urmee, T. (2014). Generating renewable energy from oil palm biomass in Malaysia: The Feed-in Tariff policy framework. biomass and bioenergy, 62, 37-46.

Unjan, R., Nissapa, A., & Phitthayaphinant, P. (2013). An identification of impacts of area expansion policy of oil palm in Southern Thailand: A case study in phatthalung and nakhon si thammarat provinces. Procedia-Social and Behavioral Sciences, 91, 489-496.


Refbacks

  • There are currently no refbacks.