International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Novian Adi Prasetyoa, Pranowob and Albertus Joko Santosob*

aDepartement of Informatics Engineering, Institut Teknologi Telkom Purwokerto, Jalan D.I. Panjaitan, Indonesia
bMagister Teknik Informatika, Universitas Atma Jaya Yogyakarta, Jalan Babarsari, Yogyakarta, Indonesia


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Indonesia is the largest industry of crude palm oil (CPO) center in the world after Malaysia and Thailand [1]. Based on data from the Ministry of Agriculture 2018 [2], Indonesia produced 27.78 million tons CPO in 2013 and increased to 37.81 million tons in 2017, with an average growth of 2.13% per year in the period 2013-2017. In 2013, the value of Indonesia's CPO exports to the world amounted to USD 17.67 million or 59.97% of Indonesia's total plantation commodity exports and increased to USD 21.25 million or 66.81% in 2017 with an average growth of 26.41% per year. CPO is a source of non-oil foreign exchange for Indonesia, so the enhancement of CPO production in Indonesia is expected to improve the welfare of the nation.

During 2013-2017, the growth of the area of oil palm plantations in Indonesia decreased -0.52% [2], the decline is expected not to affect the amount of CPO production. One of the things that affect CPO production is the primary raw material availability of palm fresh fruit bunches (FFB). The raw material availability of FFB can be predicted by several methods such as Fuzzy Rule-Based Time Series Method [3] and linear regression [4]. Both forecasting methods only calculate the needs of raw material and cannot predict the availability of real raw material. Farmers can do calculations from close range and long distance. The close range calculation of the uneven and large FFB has been done manually and it is quite difficult and spends a lot of time to do. Long distance calculations certainly have a higher level of difficulty. In addition to limited vision, loss of concentration also increases the difficulty of distance calculation.

Keywords: Palm oil fresh fruit bunches (FFB); Faster R-CNN; computer vision; object detection.

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Received: 2019-12-05
Revised: 2020-02-29
Accepted: 2020-03-15
Available Online: 2020-06-01

Cite this article:

Prasetyoa, N.A., Pranowob, Santosob, A.J. 2020. Automatic detection and calculation of palm oil fresh fruit bunches using faster R-CNN. International Journal of Applied Science and Engineering, 17, 121–134.