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Computer Vision for Food Nutrition Assessment: A Bibliometric Analysis and Technical Review

Journal of Robotics and Control (JRC) · Vol. 6 No. 5 (2025) · hlm. 2141–2151

DOI
10.18196/jrc.v6i5.27525
ISSN
2715-5072, 2715-5056
Terbit
2025-08-21
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This study examines the latest trends, challenges, and advances in food image segmentation and computer vision-based nutritional analysis. Traditional nutritional assessment methods such as food diaries and questionnaires are limited by their reliance on participant recall and manual processing, which reduces their accuracy and efficiency. As an alternative, advances in machine learning and deep learning have shown potential in automating food identification and estimating nutrient content, such as calories, protein, carbohydrates, and fat. This study was conducted through bibliometric analysis and technical review of publications from the Scopus database, using a structured search strategy and applying inclusion and exclusion criteria. Articles were selected based on topic relevance, use of machine learning or deep learning methods, publication in English, and publication between 2020 and 2024. The review identified key research trends, key contributors, popular methods such as CNN and YOLO, and the most frequently reported limitations, including lack of dataset diversity, inaccuracy in food volume estimation, and the need for real-time integrated systems. These limitations were analyzed based on the methodology and findings of the reviewed studies. This review is expected to be a comprehensive reference for researchers and practitioners in developing food image segmentation technology for more accurate and applicable nutritional assessment.

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30 penulis · 79 tautan kepenulisan bersama · 10 lintas organisasi

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  • Diponegoro University
  • Universitas Bina Sarana Informatika
  • State University of Semarang
  • University of Indonesia
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Sri Achadi NugraheniDiponegoro University4
Suyatno SuyatnoDiponegoro University3
Siti Fatimah PradigdoDiponegoro University2
Ani MargawatiDiponegoro University2
Etika Ratna NoerDiponegoro University2
Lia Awwalia MajidaDiponegoro University2
Cahya Tri PurnamiDiponegoro University2
Mahalul AzamState University of Semarang1
Mateus Sakundarno AdiDiponegoro University1
Dwi SutiningsihDiponegoro University1
Akmaliya HayuningsihDiponegoro University1
Suhartono SuhartonoDiponegoro University1
Henry Setyawan SusantoDiponegoro University1
Ligar Tresna DharmawanAfiliasi tidak diketahui1
Hanifa Maher DennyDiponegoro University1
Dion Zein NuridzinDiponegoro University1
Alfi Fairuz AsnaDiponegoro University1
Siti FatimahDiponegoro University1
Hasna Izdihar LatifahUniversity of Indonesia1
Lilis WijayantiDiponegoro University1
Sulicha NurhayatiDiponegoro University1
Gandha Sunaryo PutraDiponegoro University1
SuhartonoDiponegoro University1
Hijra HijraDiponegoro University1
Siti Fatimah-MuisDiponegoro University1
Suhartono SuhartonoDiponegoro University1
Rifatul MasrikhiyahDiponegoro University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Computer Vision for Food Nutrition Assessment: A Bibliometric Analysis and Technical Review</dc:title>
	<dc:creator>Purwati, Nani</dc:creator>
	<dc:creator>Isnanto, R. Rizal</dc:creator>
	<dc:creator>Kartasurya, Martha Irene</dc:creator>
	<dc:subject xml:lang="en-US">Bibliometric Analysis</dc:subject>
	<dc:subject xml:lang="en-US">Food Image Segmentation</dc:subject>
	<dc:subject xml:lang="en-US">Nutrition Analysis</dc:subject>
	<dc:subject xml:lang="en-US">Computer Vision</dc:subject>
	<dc:subject xml:lang="en-US">Research Trends</dc:subject>
	<dc:description xml:lang="en-US">This study examines the latest trends, challenges, and advances in food image segmentation and computer vision-based nutritional analysis. Traditional nutritional assessment methods such as food diaries and questionnaires are limited by their reliance on participant recall and manual processing, which reduces their accuracy and efficiency. As an alternative, advances in machine learning and deep learning have shown potential in automating food identification and estimating nutrient content, such as calories, protein, carbohydrates, and fat. This study was conducted through bibliometric analysis and technical review of publications from the Scopus database, using a structured search strategy and applying inclusion and exclusion criteria. Articles were selected based on topic relevance, use of machine learning or deep learning methods, publication in English, and publication between 2020 and 2024. The review identified key research trends, key contributors, popular methods such as CNN and YOLO, and the most frequently reported limitations, including lack of dataset diversity, inaccuracy in food volume estimation, and the need for real-time integrated systems. These limitations were analyzed based on the methodology and findings of the reviewed studies. This review is expected to be a comprehensive reference for researchers and practitioners in developing food image segmentation technology for more accurate and applicable nutritional assessment.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2025-08-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/27525</dc:identifier>
	<dc:identifier>10.18196/jrc.v6i5.27525</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 6 No. 5 (2025); 2141-2151</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/27525/11827</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Nani Purwati, R. Rizal Isnanto, Martha Irene Kartasurya</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>