Robotic Vision System Design for Black Pepper Harvesting

Robotic vision system design is developed in this paper to locate the coordinate of pepper fruits from trees and leaves, and identify pepper ripeness for harvest in Sarawak region, Malaysia. The vision system comprises of three stages, i.e. salient point localization, contour extraction and pepper v...

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Main Authors: Lim, King Hann, Gopalai, Alpha Agape
Other Authors: IEEE
Format: Conference Paper
Published: IEEE 2013
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/12048
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author Lim, King Hann
Gopalai, Alpha Agape
author2 IEEE
author_facet IEEE
Lim, King Hann
Gopalai, Alpha Agape
author_sort Lim, King Hann
building Curtin Institutional Repository
collection Online Access
description Robotic vision system design is developed in this paper to locate the coordinate of pepper fruits from trees and leaves, and identify pepper ripeness for harvest in Sarawak region, Malaysia. The vision system comprises of three stages, i.e. salient point localization, contour extraction and pepper verification. First, ripe peppers are spotted using visual saliency detection based on color, intensity and orientation. Three most salient regions are then determined by red component detection, whereas red element indicates a ripe pepper region. The detected red salient region is therefore shrunk to pepper edges using active contour method. To further verify the correct detection of peppers, the extracted edges are required to match with pre-defined shape, and to check neighborhoods similarity surrounding the region of interest. Preliminary simulation results showed that the vision system spotted the salient regions with pepper in 91.3% of success rate; contour extractions covering a pepper boundary with 84.35% of success rate and the results for pepper verification stage are promising.
first_indexed 2025-11-14T06:57:36Z
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T06:57:36Z
publishDate 2013
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spelling curtin-20.500.11937-120482017-09-13T14:59:18Z Robotic Vision System Design for Black Pepper Harvesting Lim, King Hann Gopalai, Alpha Agape IEEE Agriculture Pepper Harvesting Autonomous Machine Vision System Robotic vision system design is developed in this paper to locate the coordinate of pepper fruits from trees and leaves, and identify pepper ripeness for harvest in Sarawak region, Malaysia. The vision system comprises of three stages, i.e. salient point localization, contour extraction and pepper verification. First, ripe peppers are spotted using visual saliency detection based on color, intensity and orientation. Three most salient regions are then determined by red component detection, whereas red element indicates a ripe pepper region. The detected red salient region is therefore shrunk to pepper edges using active contour method. To further verify the correct detection of peppers, the extracted edges are required to match with pre-defined shape, and to check neighborhoods similarity surrounding the region of interest. Preliminary simulation results showed that the vision system spotted the salient regions with pepper in 91.3% of success rate; contour extractions covering a pepper boundary with 84.35% of success rate and the results for pepper verification stage are promising. 2013 Conference Paper http://hdl.handle.net/20.500.11937/12048 10.1109/TENCON.2013.6718825 IEEE restricted
spellingShingle Agriculture
Pepper Harvesting
Autonomous Machine
Vision System
Lim, King Hann
Gopalai, Alpha Agape
Robotic Vision System Design for Black Pepper Harvesting
title Robotic Vision System Design for Black Pepper Harvesting
title_full Robotic Vision System Design for Black Pepper Harvesting
title_fullStr Robotic Vision System Design for Black Pepper Harvesting
title_full_unstemmed Robotic Vision System Design for Black Pepper Harvesting
title_short Robotic Vision System Design for Black Pepper Harvesting
title_sort robotic vision system design for black pepper harvesting
topic Agriculture
Pepper Harvesting
Autonomous Machine
Vision System
url http://hdl.handle.net/20.500.11937/12048