Implementation of AlexNet and Xception Architectures for Disease Detection in Orange Plants
Oranges are one of Indonesia's primary horticultural commodities, with production increasing each year. However, pest and disease infestations often go undetected, leading to significant reductions in crop yields. This study implements Convolutional Neural Network (CNN) technology to identify d...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2024-11-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8700 |
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| _version_ | 1846135902424793088 |
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| author | Venus Al Fatah Moh. Ali Romli |
| author_facet | Venus Al Fatah Moh. Ali Romli |
| author_sort | Venus Al Fatah |
| collection | DOAJ |
| description | Oranges are one of Indonesia's primary horticultural commodities, with production increasing each year. However, pest and disease infestations often go undetected, leading to significant reductions in crop yields. This study implements Convolutional Neural Network (CNN) technology to identify diseases in orange plants using two architectures: AlexNet and Xception. The implementation results show that the Xception architecture achieved a high accuracy of 96% after 100 training epochs, indicating its effectiveness in disease detection tasks. This research highlights the potential of integrating CNN technology, particularly the Xception model, into web-based systems for disease detection in orange plants. Such systems can assist farmers in maintaining crop health, improving productivity, and ensuring harvest quality. |
| format | Article |
| id | doaj-art-8cc9f94c5d0c49d7b42a953ee966e337 |
| institution | Kabale University |
| issn | 2548-6861 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-8cc9f94c5d0c49d7b42a953ee966e3372024-12-09T10:38:51ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612024-11-018257457910.30871/jaic.v8i2.87008700Implementation of AlexNet and Xception Architectures for Disease Detection in Orange PlantsVenus Al FatahMoh. Ali RomliOranges are one of Indonesia's primary horticultural commodities, with production increasing each year. However, pest and disease infestations often go undetected, leading to significant reductions in crop yields. This study implements Convolutional Neural Network (CNN) technology to identify diseases in orange plants using two architectures: AlexNet and Xception. The implementation results show that the Xception architecture achieved a high accuracy of 96% after 100 training epochs, indicating its effectiveness in disease detection tasks. This research highlights the potential of integrating CNN technology, particularly the Xception model, into web-based systems for disease detection in orange plants. Such systems can assist farmers in maintaining crop health, improving productivity, and ensuring harvest quality.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8700alexnetcnndiseaseorangesxception |
| spellingShingle | Venus Al Fatah Moh. Ali Romli Implementation of AlexNet and Xception Architectures for Disease Detection in Orange Plants Journal of Applied Informatics and Computing alexnet cnn disease oranges xception |
| title | Implementation of AlexNet and Xception Architectures for Disease Detection in Orange Plants |
| title_full | Implementation of AlexNet and Xception Architectures for Disease Detection in Orange Plants |
| title_fullStr | Implementation of AlexNet and Xception Architectures for Disease Detection in Orange Plants |
| title_full_unstemmed | Implementation of AlexNet and Xception Architectures for Disease Detection in Orange Plants |
| title_short | Implementation of AlexNet and Xception Architectures for Disease Detection in Orange Plants |
| title_sort | implementation of alexnet and xception architectures for disease detection in orange plants |
| topic | alexnet cnn disease oranges xception |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8700 |
| work_keys_str_mv | AT venusalfatah implementationofalexnetandxceptionarchitecturesfordiseasedetectioninorangeplants AT mohaliromli implementationofalexnetandxceptionarchitecturesfordiseasedetectioninorangeplants |