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Accuracy of Various Methods to Estimate Volume and Weight of Symmetrical and Non-Symmetrical Fruits using Computer Vision

Journal of ICT Research and Applications (JICTRA) · Vol. 16 No. 3 (2022) · pp. 210–225

DOI
10.5614/itbj.ict.res.appl.2022.16.3.2
ISSN
2338-5499, 2337-5787
Published
2022-12-27
Publisher
DRI - ITB
Language
eng

Abstract

Many researchers have used images to measure the volume and weight of fruits so that the measurement can be done remotely and non-contact. There are various methods for fruit volume estimation based on images, i.e., Basic Shape, Solid of Revolution, Conical Frustum, and Regression. The weight estimation generally uses Regression. This study analyzed the accuracy of these methods. Tests were done by taking images of symmetrical fruits (represented by tangerines) and non-symmetrical fruits (represented by strawberries). The images were processed using segmentation in saturation color space to get binary images. The Regression method used Diameter, Projection Area, and Perimeter as features that were extracted from the binary images. For symmetrical fruits, the best accuracy was obtained with the Linear Regression based on Diameter (LDD), which gave the highest R2 (0.96 for volume and 0.93 for weight) and the lowest RMSE (5.7 mm3 for volume and 5.3 gram for volume). For non-symmetrical fruits, the highest accuracy for non-symmetric fruits was given by the Linear Regression based on Diameter (LRD) and Linear Regression based on Area (LRA) with an R2 of 0.8 for volume and weight. The RMSE for LRD and LRA for strawberries was 3.3 mm3 for volume and 1.4 grams for weight.

Keywords

Collaboration network of the authors

15 authors · 42 co-authorship links

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  • University of Brawijaya
  • Different organisations
  • Same organisation
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Co-authorOrganisationJoint articles
Rizal MaulanaUniversity of Brawijaya5
Dahnial SyauqyUniversity of Brawijaya3
Agung Setia BudiUniversity of Brawijaya2
Eko SetiawanUniversity of Brawijaya2
Fitri UtaminingrumUniversity of Brawijaya2
Randy Cahya WihandikaUniversity of Brawijaya2
Muhammad Ali FauziUniversity of Brawijaya2
Edita Rosana WidasariUniversity of Brawijaya1
Gembong Edhi SetyawanUniversity of Brawijaya1
Benny AdiwijayaUniversity of Brawijaya1
Faizal Andy SusiloUniversity of Brawijaya1
Fahmi ErzaUniversity of Brawijaya1
Eko SetiawanUniversity of Brawijaya1
Rakhmadhany PrimanandaUniversity of Brawijaya1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Accuracy of Various Methods to Estimate Volume and Weight of Symmetrical and Non-Symmetrical Fruits using Computer Vision</dc:title>
	<dc:creator>Fitriyah, Hurriyatul</dc:creator>
	<dc:subject xml:lang="en-US">conical frustum</dc:subject>
	<dc:subject xml:lang="en-US">estimation</dc:subject>
	<dc:subject xml:lang="en-US">fruits</dc:subject>
	<dc:subject xml:lang="en-US">regression</dc:subject>
	<dc:subject xml:lang="en-US">solid of revolution</dc:subject>
	<dc:description xml:lang="en-US">Many researchers have used images to measure the volume and weight of fruits so that the measurement can be done remotely and non-contact. There are various methods for fruit volume estimation based on images, i.e., Basic Shape, Solid of Revolution, Conical Frustum, and Regression. The weight estimation generally uses Regression. This study analyzed the accuracy of these methods. Tests were done by taking images of symmetrical fruits (represented by tangerines) and non-symmetrical fruits (represented by strawberries). The images were processed using segmentation in saturation color space to get binary images. The Regression method used Diameter, Projection Area, and Perimeter as features that were extracted from the binary images. For symmetrical fruits, the best accuracy was obtained with the Linear Regression based on Diameter (LDD), which gave the highest R2 (0.96 for volume and 0.93 for weight) and the lowest RMSE (5.7 mm3 for volume and 5.3 gram for volume). For non-symmetrical fruits, the highest accuracy for non-symmetric fruits was given by the Linear Regression based on Diameter (LRD) and Linear Regression based on Area (LRA) with an R2 of 0.8 for volume and weight. The RMSE for LRD and LRA for strawberries was 3.3 mm3 for volume and 1.4 grams for weight.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2022-12-27</dc:date>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/18207</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2022.16.3.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 16 No. 3 (2022); 210-225</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2022.16.3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/18207/6025</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Journal of ICT Research and Applications</dc:rights>
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