Integration of the MobileNetV2 Convolutional Neural Network Architecture into a Sorting System for Citrus Fruit Maturity Classification
DOI:
https://doi.org/10.66341/fusion.v3i1.359Keywords:
Deep Learning, Convolutional Neural Network, Citra Digital, ESP32, Otomatiasi Penyortiran Buah JerukAbstract
This research aims to develop an automated sorting system for post-harvest oranges to address subjectivity and the risk of errors caused by human fatigue. The method employed involves developing an automated sorting conveyor prototype based on an ESP32 microcontroller integrated with deep learning technology via a webcam. The continuous ripeness classification process utilizes a Convolutional Neural Network (CNN) with the MobileNetV2 architecture to categorize the fruit into ripe and half-ripe stages. The detection results are then transmitted serially to the ESP32 to drive the sorting servo in real-time. Based on testing with 50 orange samples, the MobileNetV2 CNN model achieved an accuracy of 90% with an F1-Score of 0.90. The implementation of this system is proven to significantly increase post-harvest productivity by reducing processing time by 63.3% compared to manual methods. Additionally, the data counter feature for the Ripe and Half-Ripe categories operates simultaneously to standardize production output monitoring. This study demonstrates that the integration of MobileNetV2 and ESP32 is effectively applied to a prototype-scale sorting system.
References
[1] A. Hafiz, A. Angel Tumanggor, M. Delvin Ibo, M. Yazid Noor, K. Palendeo Sitepu, And H. Syahputra, “Deteksi Jeruk Nipis Dan Jeruk Jungga Menggunakan Algoritma Cnn,” (Jurnal Mahasiswa Teknik Informatika), Vol. 9, No. 4, Pp. 6245–6251, Aug. 2025.
[2] M. Al Khalidi, Rd. S. Handayani, I. Ismadi, L. Nazirah, And N. Nilahayati, “Analisis Kemiripan Morfologi Bagian Vegetatif Tanaman Jeruk Purut Manis Lokal Aceh Dan Jeruk Purut (Citrus Hystrix),” Jurnal Agrium, Vol. 22, No. 1, Pp. 18–26, Mar. 2025, Doi: 10.29103/Agrium.V22i1.21194.
[3] M. Riziq Sirfatullah Alfarizi, M. Zidan Al-Farish, M. Taufiqurrahman, G. Ardiansah, And M. Elgar, “Penggunaan Python Sebagai Bahasa Pemrograman Untuk Machine Learning Dan Deep Learning,” Karimah Tauhid, Vol. 2, No. 1, Pp. 1–6, 2023.
[4] B. Yanto, L. Fimawahib, A. Supriyanto, B. Herawan Hayadi, And R. Rizki Pratama, “Klasifikasi Tekstur Kematangan Buah Jeruk Manis Berdasarkan Tingkat Kecerahan Warna Dengan Metode Deep Learning Convolutional Neural Network,” Inovtek Polbeng - Seri Informatika, Vol. 6, No. 2, Pp. 259–268, 2021.
[5] S. Siagian, K. Ibnutama, And R. Mahyuni, “Implementasi Metode Ekstraksi Ciri Warna Untuk Mendeteksi Kematangan Buah Jeruk,” Jurnal Sistem Informasi Triguna Dharma (Jursi Tgd), Vol. 1, No. 6, P. 898, Nov. 2022, Doi: 10.53513/Jursi.V1i6.5861.
[6] A. C. Hartono And A. R. Muslikh, “Penerapan Transfer Learning Mobilenetv2 Pada Klasifikasi Citra Jenis Buah-Buahan,” Journal Of Information System And Application Development, Vol. 3, No. 2, Pp. 103–111, 2025, Doi: 10.26905/Jisad.V3i2.16187.
[7] J. Vitasari, R. R. Nugroho, Muhammad Andra Kusuma Ramadhan, Owen Pratama Endramawan, And Mochammad Rifki Ulil Albaab, “Smart Conveyor Real-Time Sort Rotten Tomatoes With Deep Learning Method Integrated Iot Control,” Jurnal Ilmiah Research And Development Student, Vol. 3, No. 1, Pp. 242–255, Jan. 2025, Doi: 10.59024/Jis.V3i1.1135.
[8] R. Singh, C. Nickhil, K. Upendar, P. Mishra, And S. Chandra Deka, “Deep Learning-Based Approach For Classifying Mandarin Orange Maturity,” Sustainable Food Technology, Nov. 2025, Doi: 10.1039/D5fb00408j.
[9] M. Mukhofifah And E. Nurraharjo, “Sistem Deteksi Kematangan Buah Alpukat Menggunakan Metode Pengolahan Citra,” Jurnal Dinamika Informatika, Vol. 11, No. 1, Pp. 12–23, Mar. 2019, Doi: 10.35315/Informatika.V11i1.8144.
[10] Septian Dwi Chandra, Hardian Oktavianto, And Ari Eko Wardoyo, “Klasifikasi Malware Menggunakan Metode Convolutional Neural Network (Cnn) Berbasis Website,” Jurnal Penelitian Teknologi Informasi Dan Sains, Vol. 2, No. 2, Pp. 84–99, May 2024, Doi: 10.54066/Jptis.V2i2.1931.
[11] R. F. Christianti, “Estimasi Jarak Berbasis Convolutional Neural Network Dan Regresi Dalam Sistem Deteksi Kendaraan Udara Nirawak Berbasis Suara,” Jurnal Sinta: Sistem Informasi Dan Teknologi Komputasi, Vol. 2, No. 3, Pp. 153–162, Aug. 2025, Doi: 10.61124/Sinta.V2i3.92.
[12] S. Sathyanarayanan, “Confusion Matrix-Based Performance Evaluation Metrics,” African Journal Of Biomedical Research, Pp. 4023–4031, Nov. 2024, Doi: 10.53555/Ajbr.V27i4s.4345.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Fitri Aisah Pohan, Romy Aulia, Syukriadi (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
