Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images
Anthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) an...
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| Format: | contributionToPeriodical |
| Language: | Inglés |
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Elsevier
2022
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| Online Access: | http://hdl.handle.net/20.500.11939/7962 https://www.sciencedirect.com/science/article/pii/S0168169921002696 |
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| author | Fazari, Antonio Pellicer-Valero, Óscar Gómez-Sanchís, Juan Bernardi, Bruno Cubero, Sergio Benalia, Souraya Zimbalatti, Giuseppe Blasco, José |
| author_browse | Benalia, Souraya Bernardi, Bruno Blasco, José Cubero, Sergio Fazari, Antonio Gómez-Sanchís, Juan Pellicer-Valero, Óscar Zimbalatti, Giuseppe |
| author_facet | Fazari, Antonio Pellicer-Valero, Óscar Gómez-Sanchís, Juan Bernardi, Bruno Cubero, Sergio Benalia, Souraya Zimbalatti, Giuseppe Blasco, José |
| author_sort | Fazari, Antonio |
| collection | ReDivia |
| description | Anthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. Hyperspectral images (450–1050 nm) of each olive were acquired until visual symptoms of the disease were observed, in some cases up to 9 days. The olives were classified into two classes: control, inoculated with water, and fungi composed of olives inoculated with the fungus. The ResNet101 architecture was chosen and adapted to process 61-band hyperspectral images with only two classes. The result showed that the applied model is very effective in detecting infected olives since the sensitivity of the method was very high from the beginning (85% on day 3 and 100% onwards). From a commercial point of view, these results align with the need to detect the maximum number of infected fruits. |
| format | contributionToPeriodical |
| id | ReDivia7962 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | ReDivia79622025-04-25T14:48:45Z Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images Fazari, Antonio Pellicer-Valero, Óscar Gómez-Sanchís, Juan Bernardi, Bruno Cubero, Sergio Benalia, Souraya Zimbalatti, Giuseppe Blasco, José Quality inspection Spectral imaging N01 Agricultural engineering U30 Research methods H20 Plant diseases Olea europaea quality Fungi Computer vision Anthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. Hyperspectral images (450–1050 nm) of each olive were acquired until visual symptoms of the disease were observed, in some cases up to 9 days. The olives were classified into two classes: control, inoculated with water, and fungi composed of olives inoculated with the fungus. The ResNet101 architecture was chosen and adapted to process 61-band hyperspectral images with only two classes. The result showed that the applied model is very effective in detecting infected olives since the sensitivity of the method was very high from the beginning (85% on day 3 and 100% onwards). From a commercial point of view, these results align with the need to detect the maximum number of infected fruits. 2022-03-15T12:41:18Z 2022-03-15T12:41:18Z 2021 contributionToPeriodical Fazari, A., Pellicer-Valero, O. J., Gómez-Sanchıs, J., Bernardi, B., Cubero, S., Benalia, S. et al.(2021). Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images. Computers and Electronics in Agriculture, 187, 106252. 0168-1699 http://hdl.handle.net/20.500.11939/7962 10.1016/j.compag.2021.106252 https://www.sciencedirect.com/science/article/pii/S0168169921002696 en info:eu-repo/grantAgreement/ERDF/POCV 2014-2020/51918 This work is co-funded by the projects AEI PID2019-107347RRC31, PID2019-107347RR-C33, IVIA-GVA 51918 and the European Union through the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2014–2020. Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ openAccess Elsevier electronico |
| spellingShingle | Quality inspection Spectral imaging N01 Agricultural engineering U30 Research methods H20 Plant diseases Olea europaea quality Fungi Computer vision Fazari, Antonio Pellicer-Valero, Óscar Gómez-Sanchís, Juan Bernardi, Bruno Cubero, Sergio Benalia, Souraya Zimbalatti, Giuseppe Blasco, José Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images |
| title | Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images |
| title_full | Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images |
| title_fullStr | Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images |
| title_full_unstemmed | Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images |
| title_short | Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images |
| title_sort | application of deep convolutional neural networks for the detection of anthracnose in olives using vis nir hyperspectral images |
| topic | Quality inspection Spectral imaging N01 Agricultural engineering U30 Research methods H20 Plant diseases Olea europaea quality Fungi Computer vision |
| url | http://hdl.handle.net/20.500.11939/7962 https://www.sciencedirect.com/science/article/pii/S0168169921002696 |
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