Corn Leaf Disease Recognition System Using Convolutional Neural Network With The Implementation of Xception Model
Monitoring a plant's health and looking for signs of infection are two highly important aspects of sustainable agriculture. Monitoring plant diseases by manually is an extremely time-consuming and tedious task. It takes a significant amount of time, a substantial amount of labor, as well as kno...
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| Format: | Article |
| Language: | English |
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Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis
2023-09-01
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| Series: | Journal of Computing Research and Innovation |
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| Online Access: | https://jcrinn.com/index.php/jcrinn/article/view/368 |
| _version_ | 1797660722812944384 |
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| author | Iman Hazwam Abd Halim Wan Nurul Izzah Abd Hadi Muhammad Nabil Fikri Jamaluddin Ros Syamsul Hamid |
| author_facet | Iman Hazwam Abd Halim Wan Nurul Izzah Abd Hadi Muhammad Nabil Fikri Jamaluddin Ros Syamsul Hamid |
| author_sort | Iman Hazwam Abd Halim |
| collection | DOAJ |
| description | Monitoring a plant's health and looking for signs of infection are two highly important aspects of sustainable agriculture. Monitoring plant diseases by manually is an extremely time-consuming and tedious task. It takes a significant amount of time, a substantial amount of labor, as well as knowledge in plant diseases to achieve. Image processing is thus used in the process of detecting plant diseases. This project mainly focuses on corn leaves disease recognition using convolutional neural network. The Xception model, which is a part of a convolutional neural network capable of classifying images into broad object categories, would be the model of choice for this image classification. Using Convolutional Neural Network (CNN), this study aims to build and test an image classification system for identifying corn leaf diseases recognition. This research dataset is trained by analyzing a big dataset that contains pictures of various diseases that might affect corn leaves as well as pictures of corn leaves that are healthy in order to precisely identify them. The data were then analysed using a methodology known as the Agile model, which included phases for planning, requirement analysis, design, development, testing, and documentation. The findings from the study provide evidence on the precision with which the Xception model performs when applied to the datasets that have been gathered. Strongly, the results of the study will emphasize the need for developing a thorough image classification system in detecting plant diseases without human intervention. |
| first_indexed | 2024-03-11T18:34:05Z |
| format | Article |
| id | doaj.art-88e9e33daca845ce838ee5910909962a |
| institution | Directory Open Access Journal |
| issn | 2600-8793 |
| language | English |
| last_indexed | 2024-03-11T18:34:05Z |
| publishDate | 2023-09-01 |
| publisher | Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis |
| record_format | Article |
| series | Journal of Computing Research and Innovation |
| spelling | doaj.art-88e9e33daca845ce838ee5910909962a2023-10-13T06:03:03ZengFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA PerlisJournal of Computing Research and Innovation2600-87932023-09-018218919910.24191/jcrinn.v8i2.368368Corn Leaf Disease Recognition System Using Convolutional Neural Network With The Implementation of Xception ModelIman Hazwam Abd Halim0Wan Nurul Izzah Abd Hadi1Muhammad Nabil Fikri Jamaluddin2Ros Syamsul Hamid3Universiti Teknologi MARA, Perlis BranchUniversiti Teknologi MARA, Perlis BranchUiTM Cawangan Perlis Kampus ArauUiTM Cawangan Perlis Kampus ArauMonitoring a plant's health and looking for signs of infection are two highly important aspects of sustainable agriculture. Monitoring plant diseases by manually is an extremely time-consuming and tedious task. It takes a significant amount of time, a substantial amount of labor, as well as knowledge in plant diseases to achieve. Image processing is thus used in the process of detecting plant diseases. This project mainly focuses on corn leaves disease recognition using convolutional neural network. The Xception model, which is a part of a convolutional neural network capable of classifying images into broad object categories, would be the model of choice for this image classification. Using Convolutional Neural Network (CNN), this study aims to build and test an image classification system for identifying corn leaf diseases recognition. This research dataset is trained by analyzing a big dataset that contains pictures of various diseases that might affect corn leaves as well as pictures of corn leaves that are healthy in order to precisely identify them. The data were then analysed using a methodology known as the Agile model, which included phases for planning, requirement analysis, design, development, testing, and documentation. The findings from the study provide evidence on the precision with which the Xception model performs when applied to the datasets that have been gathered. Strongly, the results of the study will emphasize the need for developing a thorough image classification system in detecting plant diseases without human intervention.https://jcrinn.com/index.php/jcrinn/article/view/368disease recognitioncorn leafconvolutional neural networksxception model |
| spellingShingle | Iman Hazwam Abd Halim Wan Nurul Izzah Abd Hadi Muhammad Nabil Fikri Jamaluddin Ros Syamsul Hamid Corn Leaf Disease Recognition System Using Convolutional Neural Network With The Implementation of Xception Model Journal of Computing Research and Innovation disease recognition corn leaf convolutional neural networks xception model |
| title | Corn Leaf Disease Recognition System Using Convolutional Neural Network With The Implementation of Xception Model |
| title_full | Corn Leaf Disease Recognition System Using Convolutional Neural Network With The Implementation of Xception Model |
| title_fullStr | Corn Leaf Disease Recognition System Using Convolutional Neural Network With The Implementation of Xception Model |
| title_full_unstemmed | Corn Leaf Disease Recognition System Using Convolutional Neural Network With The Implementation of Xception Model |
| title_short | Corn Leaf Disease Recognition System Using Convolutional Neural Network With The Implementation of Xception Model |
| title_sort | corn leaf disease recognition system using convolutional neural network with the implementation of xception model |
| topic | disease recognition corn leaf convolutional neural networks xception model |
| url | https://jcrinn.com/index.php/jcrinn/article/view/368 |
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