Dr. Liyantono Liyantono Profile

Dr. Liyantono Liyantono

at IPB Univ

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Proceedings Article | 24 December 2019 Paper

Algorithm of pattern recognition for real-time rice crops monitoring using Sentinel images

. Jailani, . Liyantono, Yudi Setiawan, Hengki Muradi, Sri Muslimah

Proceedings Volume 11372, 1137204 (2019) https://doi.org/10.1117/12.2541801

KEYWORDS: Vegetation, Satellites, Satellite imaging, Earth observing sensors, Raster graphics, Image processing, Detection and tracking algorithms, Spatial resolution, Pattern recognition, Near infrared

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Rice crops is a crucial commodity for Indonesian people. Most Indonesian people consume rice as a staple food. With the rate of population growth that continues to increase, the demand for rice also increases. Rice crops Monitoring needed in ensuring national food availability. This paper presents a new method for predicting the growth phase of rice crops with a rice field area approach and identifying patterns of vegetation index from Sentinel 2A satellite image data series from May to September 2017. The study was conducted in Sukamekar Village, Karawang District, West Java, Indonesia from June to August 2017. Polynomial regression models used to identify the relationship between the growth phase of rice crops and vegetation index. The vegetation index used is NDVI. In determining the vegetation index in the rice field two methods are obtained, first calculating the average value of the vegetation index on the pixels in each rice fields area. Second, by removing the pixels that contact with the border of each rice field area, then calculate the average value of the vegetation index on the pixel of the rice field area. From both of methods, an algorithm was developed to get the rule base to determine the phase of rice growth based on the value of the vegetation index in each field area. The model developed was implemented in the same location in January 2019 using Sentinel-2A Image. Based on field validation in February 2019, the accuracy of the first method was 70% while the second method was 75%.

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