Unsupervised anomaly detection for pome fruit quality inspection using X-ray radiography
[Display omitted] •X-ray radiographs were used to identify apple and pear fruit with internal disorders.•Classification was achieved by a fully convolutional autoencoder.•We evaluated our model on both simulated and real data.•Our model outperforms the traditional autoencoder architecture. A novel f...
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| Vydáno v: | Computers and electronics in agriculture Ročník 226; s. 109364 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.11.2024
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| Témata: | |
| ISSN: | 0168-1699 |
| On-line přístup: | Získat plný text |
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| Abstract | [Display omitted]
•X-ray radiographs were used to identify apple and pear fruit with internal disorders.•Classification was achieved by a fully convolutional autoencoder.•We evaluated our model on both simulated and real data.•Our model outperforms the traditional autoencoder architecture.
A novel fully convolutional autoencoder (convAE) was introduced to analyze X-ray radiography images of ‘Braeburn’ apples and ‘Conference’ pears with and without disorders for online sorting purposes. The model was solely trained on either apple or pear samples without disorders and outperformed a traditional autoencoder (AE) across multiple test sets. We evaluated our approach using the area under the curve (AUC) as an evaluation metric. A cross-test experiment further demonstrated consistent performance between a model trained on apple data for classifying pear fruit (accuracy: 71 %) and a pear-specific model (accuracy: 70 %). We also evaluated models trained on simulated X-ray radiographs with real ones, and vice versa. For instance, under scenario of training on real data and testing on simulated X-ray radiographs, an accuracy of 80 % for detecting disordered non-consumable pear was achieved. This work provides valuable insights into anomaly detection for apples and pears with several disorders. |
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| AbstractList | [Display omitted]
•X-ray radiographs were used to identify apple and pear fruit with internal disorders.•Classification was achieved by a fully convolutional autoencoder.•We evaluated our model on both simulated and real data.•Our model outperforms the traditional autoencoder architecture.
A novel fully convolutional autoencoder (convAE) was introduced to analyze X-ray radiography images of ‘Braeburn’ apples and ‘Conference’ pears with and without disorders for online sorting purposes. The model was solely trained on either apple or pear samples without disorders and outperformed a traditional autoencoder (AE) across multiple test sets. We evaluated our approach using the area under the curve (AUC) as an evaluation metric. A cross-test experiment further demonstrated consistent performance between a model trained on apple data for classifying pear fruit (accuracy: 71 %) and a pear-specific model (accuracy: 70 %). We also evaluated models trained on simulated X-ray radiographs with real ones, and vice versa. For instance, under scenario of training on real data and testing on simulated X-ray radiographs, an accuracy of 80 % for detecting disordered non-consumable pear was achieved. This work provides valuable insights into anomaly detection for apples and pears with several disorders. |
| ArticleNumber | 109364 |
| Author | Tempelaere, Astrid Nicolai, Bart Valerio Giuffrida, Mario Van Doorselaer, Leen He, Jiaqi Verboven, Pieter |
| Author_xml | – sequence: 1 givenname: Astrid orcidid: 0000-0003-3807-6695 surname: Tempelaere fullname: Tempelaere, Astrid email: astrid.tempelaere@kuleuven.be organization: KU Leuven, MeBioS-BIOSYST, Willem De Croylaan 42, 3001 Leuven, Belgium – sequence: 2 givenname: Jiaqi surname: He fullname: He, Jiaqi email: jiaqi.he@kuleuven.be organization: KU Leuven, MeBioS-BIOSYST, Willem De Croylaan 42, 3001 Leuven, Belgium – sequence: 3 givenname: Leen surname: Van Doorselaer fullname: Van Doorselaer, Leen email: leen.vandoorselaer@kuleuven.be organization: KU Leuven, MeBioS-BIOSYST, Willem De Croylaan 42, 3001 Leuven, Belgium – sequence: 4 givenname: Pieter surname: Verboven fullname: Verboven, Pieter email: pieter.verboven@kuleuven.be organization: KU Leuven, MeBioS-BIOSYST, Willem De Croylaan 42, 3001 Leuven, Belgium – sequence: 5 givenname: Bart orcidid: 0000-0001-9542-8285 surname: Nicolai fullname: Nicolai, Bart email: bart.nicolai@kuleuven.be organization: KU Leuven, MeBioS-BIOSYST, Willem De Croylaan 42, 3001 Leuven, Belgium – sequence: 6 givenname: Mario surname: Valerio Giuffrida fullname: Valerio Giuffrida, Mario email: valerio.giuffrida@nottingham.ac.uk organization: University of Nottingham, Computer Vision Laboratory, Wollaton Road, NG8 1BB Nottingham, United Kingdom |
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| Cites_doi | 10.1016/j.postharvbio.2022.111950 10.1016/j.jfoodeng.2020.110102 10.1007/978-3-319-70096-0_39 10.2214/AJR.14.13116 10.1016/j.postharvbio.2023.112342 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3 10.1109/ISIE45552.2021.9576231 10.1016/S0925-5214(98)00035-0 10.1016/j.compag.2018.08.032 10.1016/j.postharvbio.2023.112439 10.1016/j.eswa.2021.114925 10.1016/j.compbiomed.2022.106328 10.5943/ppq/7/1/8 10.1016/S0925-5214(01)00120-X 10.1016/j.postharvbio.2023.112390 10.1016/j.postharvbio.2015.12.015 10.1117/12.262857 10.1007/978-3-319-59050-9_12 10.1016/j.compag.2022.106962 10.1016/j.ultramic.2015.05.002 10.1016/j.postharvbio.2012.08.008 10.1016/j.postharvbio.2013.08.008 10.1063/1.4963604 10.1016/j.foodcont.2023.110092 10.1109/TIP.2003.819861 10.1016/j.postharvbio.2015.09.020 10.1111/1541-4337.12269 10.1016/j.postharvbio.2024.112953 10.1016/j.scienta.2022.110943 10.1109/JPROC.2021.3052449 10.19071/cb.2017.v8.3211 10.1364/OE.24.025129 10.3390/s20236753 10.1016/j.postharvbio.2023.112576 10.1146/annurev-food-030713-092410 10.3390/geomatics3010004 10.1016/j.compag.2019.104948 10.1016/j.postharvbio.2018.05.020 |
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| Keywords | Deep learning Food quality inspection Autoencoder Anomaly detection X-ray imaging |
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