Universal Journal of Agricultural Research Vol. 11(6), pp. 1077 - 1088
DOI: 10.13189/ujar.2023.110615
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Coffee Land Mapping Using Unmanned Aerial Vehicles (UAVs) Multispectral Remote Sensing

Ade Astri Muliasari ^1,*, Zainal Wassahua ², Merry Gloria Meliala ¹, Ika Sartika ³, Reza Septian ^4
1 Technology and Management of Plantation, College of Vocational Studies, Bogor Agricultural University, Indonesia
² National Research and Innovation Agency, Republic of Indonesia, Indonesia
³ Digital Communications and Media, College of Vocational Studies, Bogor Agricultural University, Indonesia
⁴ Food Crops, Horticultural, and Plantation Office of Bogor Regency Government, Indonesia

ABSTRACT

Remote sensing UAV technology, also known as drones, is a tool that is quite well-known today as a technological transformation for land mapping that can be done directly and inexpensively every day. Currently, the use of UAVs is growing because this technology is easy to operate for crop monitoring and land mapping. Although the resulting spectral pattern is quite varied to assess vegetation, especially in mixed vegetation. The use of UAV image data will be better for identifying plants, especially for coffee if they are not covered by other dominant vegetation. This study aims to map the distribution and number of Arabica coffee plants in Megamendung Village, Bogor Regency. A multispectral remote sensing UAV can be used to effectively map coffee fields and the number of coffee plants, even on land with steep contours, if they are not covered by denser vegetation or shade trees. Based on UAV Acquisitions process, it produces a wide multispectral image of 9.68 ha (69%) in the first and the second area of 8.91 ha (78%). Coffee planting can be differentiated into < 4 years, 4 – 15 years, and > 15 years. The number of coffee plants identified at the first location was 179 immature coffee trees. While the number of coffee plants identified at the second location was 69 immature coffee trees (3%), mature coffee 1 was 1329 trees (50%), and mature coffee 2 was 1269 trees (48%) of the total number of coffee plants of 2667 coffee trees.

KEYWORDS
Coffee, Land Mapping, Multispectral, Remote Sensing

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Ade Astri Muliasari , Zainal Wassahua , Merry Gloria Meliala , Ika Sartika , Reza Septian , "Coffee Land Mapping Using Unmanned Aerial Vehicles (UAVs) Multispectral Remote Sensing," Universal Journal of Agricultural Research, Vol. 11, No. 6, pp. 1077 - 1088, 2023. DOI: 10.13189/ujar.2023.110615.

(b). APA Format:
Ade Astri Muliasari , Zainal Wassahua , Merry Gloria Meliala , Ika Sartika , Reza Septian (2023). Coffee Land Mapping Using Unmanned Aerial Vehicles (UAVs) Multispectral Remote Sensing. Universal Journal of Agricultural Research, 11(6), 1077 - 1088. DOI: 10.13189/ujar.2023.110615.