Autonomous tomato harvesting robotic system in greenhouses: deep learning classification

Solanum lycopersicum or generally known as tomato came from countries of South America and has been growing in many tropical countries and its healthy nutrients in tomato becomes one of the food demand by the locals in Malaysia when their lifestyle shifted to more concern for healthy food. Since exp...

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Main Authors:	Ooi, Peng Toon, Muhammad Aizzat, Zakaria, Ahmad Fakhri, Ab. Nasir, A. P. P., Abdul Majeed, Chung, Young Tan, Leonard Chong, Yew Ng
Format:	Article
Language:	English
Published:	Penerbit UMP 2019
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/24737/ http://umpir.ump.edu.my/id/eprint/24737/ http://umpir.ump.edu.my/id/eprint/24737/ http://umpir.ump.edu.my/id/eprint/24737/7/Autonomous%20tomato%20harvesting%20robotic.pdf

id	ump-24737
recordtype	eprints
spelling	ump-247372019-11-21T05:57:02Z http://umpir.ump.edu.my/id/eprint/24737/ Autonomous tomato harvesting robotic system in greenhouses: deep learning classification Ooi, Peng Toon Muhammad Aizzat, Zakaria Ahmad Fakhri, Ab. Nasir A. P. P., Abdul Majeed Chung, Young Tan Leonard Chong, Yew Ng TS Manufactures Solanum lycopersicum or generally known as tomato came from countries of South America and has been growing in many tropical countries and its healthy nutrients in tomato becomes one of the food demand by the locals in Malaysia when their lifestyle shifted to more concern for healthy food. Since export value and production has increased for the past few years, a vast amount of labours considered for the fruit-picking process. Hence, farmers are now preferring to look for automation to replace labour problems and high cost that they are facing. To pick a correct fruit within clusters, a harvesting robot requires guidance so that it can detect a fruit accurately. In this study, a new classification algorithm using deep learning specifically convolution neural network to classify the image is either a tomato or not tomato and next, the image is classified into either a ripe or unripe tomato. Furthermore, there are two classification neural networks which are tomato or not tomato and ripe and unripe tomato. Each network consists of 600 training data and 33 testing data. The accuracies that obtained from network 1 (tomato or not tomato) and network 2 (ripe or unripe tomato) are 76.366% and 98.788% respectively. Penerbit UMP 2019 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/24737/7/Autonomous%20tomato%20harvesting%20robotic.pdf Ooi, Peng Toon and Muhammad Aizzat, Zakaria and Ahmad Fakhri, Ab. Nasir and A. P. P., Abdul Majeed and Chung, Young Tan and Leonard Chong, Yew Ng (2019) Autonomous tomato harvesting robotic system in greenhouses: deep learning classification. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 1 (1). pp. 80-86. ISSN 2637-0883 http://journal.ump.edu.my/mekatronika/article/view/1148 https://doi.org/10.15282/mekatronika.v1i1.1148
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TS Manufactures
spellingShingle	TS Manufactures Ooi, Peng Toon Muhammad Aizzat, Zakaria Ahmad Fakhri, Ab. Nasir A. P. P., Abdul Majeed Chung, Young Tan Leonard Chong, Yew Ng Autonomous tomato harvesting robotic system in greenhouses: deep learning classification
description	Solanum lycopersicum or generally known as tomato came from countries of South America and has been growing in many tropical countries and its healthy nutrients in tomato becomes one of the food demand by the locals in Malaysia when their lifestyle shifted to more concern for healthy food. Since export value and production has increased for the past few years, a vast amount of labours considered for the fruit-picking process. Hence, farmers are now preferring to look for automation to replace labour problems and high cost that they are facing. To pick a correct fruit within clusters, a harvesting robot requires guidance so that it can detect a fruit accurately. In this study, a new classification algorithm using deep learning specifically convolution neural network to classify the image is either a tomato or not tomato and next, the image is classified into either a ripe or unripe tomato. Furthermore, there are two classification neural networks which are tomato or not tomato and ripe and unripe tomato. Each network consists of 600 training data and 33 testing data. The accuracies that obtained from network 1 (tomato or not tomato) and network 2 (ripe or unripe tomato) are 76.366% and 98.788% respectively.
format	Article
author	Ooi, Peng Toon Muhammad Aizzat, Zakaria Ahmad Fakhri, Ab. Nasir A. P. P., Abdul Majeed Chung, Young Tan Leonard Chong, Yew Ng
author_facet	Ooi, Peng Toon Muhammad Aizzat, Zakaria Ahmad Fakhri, Ab. Nasir A. P. P., Abdul Majeed Chung, Young Tan Leonard Chong, Yew Ng
author_sort	Ooi, Peng Toon
title	Autonomous tomato harvesting robotic system in greenhouses: deep learning classification
title_short	Autonomous tomato harvesting robotic system in greenhouses: deep learning classification
title_full	Autonomous tomato harvesting robotic system in greenhouses: deep learning classification
title_fullStr	Autonomous tomato harvesting robotic system in greenhouses: deep learning classification
title_full_unstemmed	Autonomous tomato harvesting robotic system in greenhouses: deep learning classification
title_sort	autonomous tomato harvesting robotic system in greenhouses: deep learning classification
publisher	Penerbit UMP
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/24737/ http://umpir.ump.edu.my/id/eprint/24737/ http://umpir.ump.edu.my/id/eprint/24737/ http://umpir.ump.edu.my/id/eprint/24737/7/Autonomous%20tomato%20harvesting%20robotic.pdf
first_indexed	2023-09-18T22:37:37Z
last_indexed	2023-09-18T22:37:37Z
_version_	1777416701154951168

Autonomous tomato harvesting robotic system in greenhouses: deep learning classification

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