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

Supattra Puttinaovarat, Paramate Horkaew, Thanyathep Saengchot, Sudarat Sinwisarn


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

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