Image Processing Application on Automatic Fruit Detection for Agriculture Industry

DOI

10.2991/ahe.k.220205.009How to use a DOI?

Keywords

Blob analysis; digital farming; edge detection; image segmentation; visual servoing

Abstract

The robot brings automation to every sector of human life, including agriculture. Automation in agriculture might be the solution to get a higher quality harvest and less dependency on human farming. The most suitable type of robot for harvesting is an arm robot manipulator. The harvesting robot needs “eye” to “see” the crop/fruit to be harvested. The detection is made possible by using image processing to get the fruit position. The fruit position is the input for a visual servoing robot. The image processing needs to be simple and effective to ensure less computational time to facilitate the limited memory of the available microcontroller. This paper proposes three image processing methods, i.e., image segmentation, edge detection, and blob analysis. The processes were conducted in SCILAB, and three fruit were used as the model, i.e., oranges, grapes, and tomato cherry. The results showed that all the fruit are detected and isolated by the vegetation background.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Tresna Dewi
AU  - Rusdianasari Rusdianasari
AU  - RD Kusumanto
AU  - Siproni Siproni
PY  - 2022
DA  - 2022/02/14
TI  - Image Processing Application on Automatic Fruit Detection for Agriculture Industry
BT  - Proceedings of the 5th FIRST T1 T2 2021 International Conference (FIRST-T1-T2 2021)
PB  - Atlantis Press
SP  - 47
EP  - 53
SN  - 2589-4943
UR  - https://doi.org/10.2991/ahe.k.220205.009
DO  - 10.2991/ahe.k.220205.009
ID  - Dewi2022
ER  -