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All Journal Fusion : Journal of Research in Engineering, Technology and Applied Sciences
Fitri Aisah Pohan
Politeknik Pertanian Negeri Payakumbuh
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Materials Science & Nanotechnology

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Integration of the MobileNetV2 Convolutional Neural Network Architecture into a Sorting System for Citrus Fruit Maturity Classification Fitri Aisah Pohan; Romy Aulia; Syukriadi Syukriadi
Fusion : Journal of Research in Engineering, Technology and Applied Sciences Vol. 3 No. 1 (2026): Fusion - April
Publisher : PT. Faaslib Serambi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66341/fusion.v3i1.359

Abstract

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.
Co-Authors Romy Aulia Syukriadi