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Agriculture
Volume 14
Issue 7
10.3390/agriculture14071098
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Article

Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production

by
Xiaotong Gao
1,
Songwei Li
1,
Xiaotong Su
1,
Yan Li
1,
Lingyun Huang
1,
Weidong Tang
1,
Yuanchen Zhang
1,2,* and
Min Dong
1,*
1
China Agricultural University, Beijing 100083, China
2
College of Biology and Food Engineering, Anyang Institute of Technology, No. 73 Huanghe Road, Anyang 455000, China
*
Authors to whom correspondence should be addressed.
Agriculture 2024, 14(7), 1098; https://doi.org/10.3390/agriculture14071098
Submission received: 10 June 2024 / Revised: 3 July 2024 / Accepted: 5 July 2024 / Published: 9 July 2024
(This article belongs to the Special Issue Comprehensive Application and Prospects of New Technologies for Plant Protection)
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Abstract

In this study, a deep learning-based system for apple defect detection and quality grading was developed, integrating various advanced image-processing technologies and machine learning algorithms to enhance the automation and accuracy of apple quality monitoring. Experimental validation demonstrated the superior performance of the proposed model in handling complex image tasks. In the defect-segmentation experiments, the method achieved a precision of 93%, a recall of 90%, an accuracy of 91% and a mean Intersection over Union (mIoU) of 92%, significantly surpassing traditional deep learning models such as U-Net, SegNet, PSPNet, UNet++, DeepLabv3+ and HRNet. Similarly, in the quality-grading experiments, the method exhibited high efficiency with a precision of 91%, and both recall and accuracy reaching 90%. Additionally, ablation experiments with different loss functions confirmed the significant advantages of the Jump Loss in enhancing model performance, particularly in addressing class imbalance and improving feature learning. These results not only validate the effectiveness and reliability of the system in practical applications but also highlight its potential in automating the detection and grading processes in the apple industry. This integration of advanced technologies provides a new automated solution for quality control of agricultural products like apples, facilitating the modernization of agricultural production.
Keywords: advanced image processing; Segment Anything Model; apple quality grading; apple defect detection; deep learning in agriculture advanced image processing; Segment Anything Model; apple quality grading; apple defect detection; deep learning in agriculture

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MDPI and ACS Style

Gao, X.; Li, S.; Su, X.; Li, Y.; Huang, L.; Tang, W.; Zhang, Y.; Dong, M. Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production. Agriculture 2024, 14, 1098. https://doi.org/10.3390/agriculture14071098

AMA Style

Gao X, Li S, Su X, Li Y, Huang L, Tang W, Zhang Y, Dong M. Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production. Agriculture. 2024; 14(7):1098. https://doi.org/10.3390/agriculture14071098

Chicago/Turabian Style

Gao, Xiaotong, Songwei Li, Xiaotong Su, Yan Li, Lingyun Huang, Weidong Tang, Yuanchen Zhang, and Min Dong. 2024. "Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production" Agriculture 14, no. 7: 1098. https://doi.org/10.3390/agriculture14071098

APA Style

Gao, X., Li, S., Su, X., Li, Y., Huang, L., Tang, W., Zhang, Y., & Dong, M. (2024). Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production. Agriculture, 14(7), 1098. https://doi.org/10.3390/agriculture14071098

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