A Lightweight YOLO-Based Architecture for Apple Detection on Embedded Systems

Juan Carlos Olguín-Rojas, Juan Irving Vasquez (), Gilberto de Jesús López-Canteñs, Juan Carlos Herrera-Lozada and Canek Mota-Delfin
Additional contact information
Juan Carlos Olguín-Rojas: Departamento de Ingeniería Mecánica Agrícola, Universidad Autónoma Chapingo, Carretera México-Texcoco km 38.5., Texcoco de Mora 56230, Mexico
Juan Irving Vasquez: Centro de Innovación y Desarrollo Tecnológico en Cómputo (CIDETEC), Instituto Politécnico Nacional (IPN), Juan de Dios Bátiz s/n esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, Del. Gustavo A. Madero, Mexico City 07738, Mexico
Gilberto de Jesús López-Canteñs: Departamento de Ingeniería Mecánica Agrícola, Universidad Autónoma Chapingo, Carretera México-Texcoco km 38.5., Texcoco de Mora 56230, Mexico
Juan Carlos Herrera-Lozada: Centro de Innovación y Desarrollo Tecnológico en Cómputo (CIDETEC), Instituto Politécnico Nacional (IPN), Juan de Dios Bátiz s/n esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, Del. Gustavo A. Madero, Mexico City 07738, Mexico
Canek Mota-Delfin: Departamento de Ingeniería Mecánica Agrícola, Universidad Autónoma Chapingo, Carretera México-Texcoco km 38.5., Texcoco de Mora 56230, Mexico

Agriculture, 2025, vol. 15, issue 8, 1-18

Abstract: Apples are among the most important agricultural products worldwide. Ensuring apple quality with minimal effort is crucial for both large-scale and local producers. In Mexico, the manual detection of damaged apples has led to inconsistencies in product quality, a problem that can be addressed by integrating vision systems with machine learning algorithms. The YOLO (You Only Look Once) neural network has significantly improved fruit detection through image processing and has automated several related tasks. However, training and deploying YOLO models typically requires substantial computational resources, making it essential to develop lightweight and cost-effective detection systems, especially for edge computing systems. This paper presents a mechatronic system designed to detect apple varieties and potential damage in apples ( Malus domestica ) within the visible spectrum. The cultivated apple varieties considered were Gala, Golden, Granny Smith, and Red Delicious. Our contribution lies in developing a lightweight neural network architecture optimized specifically for embedded systems. The proposed architecture was compared against YOLOv3-Tiny, YOLOv4-Tiny, and YOLOv5-s. Our optimized model achieved a high accuracy and sensitivity (94–99%) and was successfully implemented on a Jetson Xavier NX board, where it reached a processing speed of 37 FPS.

Keywords: machine learning; computer vision; apple quality control; YOLO; embedded systems (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/8/838/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/8/838/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:8:p:838-:d:1633708

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().