A Study of Deep Learning Method Opportunity on Palm Oil FFB (Fresh Fruit Bunch) Grading Methods

DOI

10.2991/adics-es-19.2019.9How to use a DOI?

Keywords

deep learning, palm oil, ffb, grading

Abstract

The deep learning method is a state of the art in technological developments in various fields, including in agriculture. Deep learning applications in agriculture include many things including the application of fruit grading, including the fruit of palm or palm oil FFB (Fresh Fruit Bunch). Deep learning implementation opportunity in palm oil FFB grading is open because one aspect of fresh fruit grading is based on the number of sockets (fruitless) contained in FFB. Deep learning has the ability to recognize objects, so the determination of FFB grading can be developed based on calculating sockets (fruitless) by utilizing deep learning. So far no researcher has used the number of sockets for grading FFB using deep learning.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Wahyu Aji
AU  - Kamarul Hawari
PY  - 2019/11
DA  - 2019/11
TI  - A Study of Deep Learning Method Opportunity on Palm Oil FFB (Fresh Fruit Bunch) Grading Methods
BT  - Proceedings of the 2019 Ahmad Dahlan International Conference Series on Engineering and Science (ADICS-ES 2019)
PB  - Atlantis Press
SP  - 22
EP  - 25
SN  - 2352-5401
UR  - https://doi.org/10.2991/adics-es-19.2019.9
DO  - 10.2991/adics-es-19.2019.9
ID  - Aji2019/11
ER  -