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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Edukasi dan Penelitian Informatika (JEPIN) EMITTER International Journal of Engineering Technology Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Journal of Applied Engineering and Technological Science (JAETS) eProceedings of Engineering LOGIC: Jurnal Penelitian Informatika Proceeding of Community Service and Engagement
Kurniawan Nur Ramadhani
Universitas Telkom
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Cocoa Ripeness Classification Using Vision Transformer Febryanti Sthevanie; Untari Novia Wisesty; Gia Septiana Wulandari; Kurniawan Nur Ramadhani
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i2.6663

Abstract

The quality of manual methods for assessing the ripeness of cocoa pods is subjective and varies from one person to another because of the intense labor required and variation of light and background conditions within the field. This research implemented an automated classification approach for cocoa ripeness classification utilizing Vision Transformer (ViT) with Shifted Patch Tokenization (SPT) and Locality Self Attention (LSA) to improve classification accuracy. The model proposed in this research achieved an accuracy of 82.65% and a macro F1 score of 82.71 on the exam with 1,559 images captured under varying illumination backgrounds and complex scenes. The model also proved better than baseline CNN architectures such as VGG, MobileNet, and ResNet in identifying visually progressive stages of ripeness and demonstrated greater generalization in cocoa ripeness classification. The findings of this research indicate the benefits of reducing manual intervention with careful inspection without compromising quality assurance standards in cocoa production. This work demonstrates new ways of applying transformer models to address computer vision problems in agriculture which is a step towards precision and smart farming.
Co-Authors Abdan Hafidh Ahnafi Abdul Rahim Achmad Hussein Sundawa Kartamihardja Ade Saepul Mugni Agnes Dirgahayu Palit Agnes Dirgahayu Palit Alda Putri Utami Amalina, Neneng Nur Amalina, Neneng Nur Anang Kurniawan Anditya Arifianto Arida Kartika Arie Ardiyanti Suryani Arif M Barmawi Arifudin Achmad Cherry Nasa Putra Diah Ajeng Dwi Yuniasih Dimitri Mahayana Dwiki Lazzaro Ema Rachmawati Febriyanti Sthevanie Febryanti Sthevanie Fikri Firdaus Fitri Amalia Hadi Friendly Halomoan Sipayung Gamma Kosala Gia Septiana Wulandari Hafidh Fikri Rasyid I Putu Indra Aristya Ignasius Indra Kusuma Wijaya Indra Bayu Kusuma Jondri Ketut Sudyatmika Putra Ledya Novamizanti M. Adhi Satria M. Ardi Firmansyah M.Syahrul Mubarok Maruanaya, Farhan Bary Mochamad Rakha Luthfi Fahsya Mohamad Syahrul Mubarok Mohamad Syahrul Mubarok Muh Aswan Abidin1 Muhammad Afif Amanullah Fawwaz Muhammad Hariz Arasy Muhammad Salman Farhan Naufal Ihsan Kusumayadhi Ni Gusti Ayu Mirah Eka Darmayanti Parjuangan, Sabam Pima Hani Safitri Prasti Eko Yunanto Rini Shintawati Riski Novanda Rita Rismala Sakinah Indriyani Saputra, Naufal Luthfi Sthevanie, Febryanty Suyanto Suyanto Tjokorda Agung Budi W Tjokorda Agung Budi Wirayuda Untari Novia Wisesty YUYUN SITI ROHMAH