Fusion of Acoustic and Vis-NIRS Information for High-Accuracy Online Detection of Moldy Core in Apples

Nan Chen (), Xiaoyu Zhang, Zhi Liu, Tianyu Zhang, Qingrong Lai, Bin Li, Yeqing Lu, Bo Hu, Xiaogang Jiang and Yande Liu ()
Additional contact information
Nan Chen: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 230062, China
Xiaoyu Zhang: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 230062, China
Zhi Liu: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 230062, China
Tianyu Zhang: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 230062, China
Qingrong Lai: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 230062, China
Bin Li: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 230062, China
Yeqing Lu: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 230062, China
Bo Hu: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 230062, China
Xiaogang Jiang: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 230062, China
Yande Liu: School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 230062, China

Agriculture, 2025, vol. 15, issue 11, 1-18

Abstract: Moldy core is a common disease of apples, and non-destructive, rapid and accurate detection of moldy core apples is essential to ensure food safety and reduce post-harvest economic losses. In this study, the acoustic method was used for the first time for the online detection of moldy core apples, and we explore the feasibility of integrating acoustic and visible–near-infrared spectroscopy (Vis–NIRS) technologies for precise, real-time detection of moldy core in apples. The sound and Vis–NIRS signals of apples were collected using a novel acoustic online detection device and a traditional Vis–NIRS online sorter, respectively. Based on this, traditional machine learning and deep learning classification models were developed for the prediction of healthy, mild, moderate, and severe moldy apples. The results show that the acoustic detection method significantly outperforms the Vis–NIRS method in terms of moldy apple identification accuracy, and the fusion of acoustic and Vis–NIRS data can further improve the model prediction performance. The MLP-Transformer shows the best prediction performance, with the overall classification accuracies for the fusion of Vis–NIRS, acoustic, Vis–NIRS and acoustic reached 89.66%, 96.55%, and 98.62%, respectively. This study demonstrates the excellent performance of acoustic online detection for intra-fruit lesion identification and shows the potential of the fusion of acoustics and Vis–NIRS.

Keywords: moldy core apple; acoustic; vis–NIRS; online detection; nondestructive testing; fruit quality (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/2077-0472/15/11/1202/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/11/1202/ (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:11:p:1202-:d:1669385

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 ().