A review of agriculture crop diseases detection using deep learning.
Crop diseases has been causing a lot of loss in agriculture sector. The fast and accurate diagnosis of crop diseases is crucial in preventing and limiting loss from the crop diseases. To achieve this goal, method such as deep learning can be used to detect crop diseases. In this study, we review and...
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2022
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| Online Access: | http://eprints.utm.my/104574/1/MohammadSolihinKadirSyahidAnuar2022_AReviewofAgricultureCropDiseasesDetection.pdf |
| _version_ | 1796867684280827904 |
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| author | Mohd. Anuar, Mohd. Syahid Kadir, Muhammad Solihin |
| author_facet | Mohd. Anuar, Mohd. Syahid Kadir, Muhammad Solihin |
| author_sort | Mohd. Anuar, Mohd. Syahid |
| collection | ePrints |
| description | Crop diseases has been causing a lot of loss in agriculture sector. The fast and accurate diagnosis of crop diseases is crucial in preventing and limiting loss from the crop diseases. To achieve this goal, method such as deep learning can be used to detect crop diseases. In this study, we review and study the performance of three convolutional neural network model, which is VGG16, VGG19 and Resnet50 model to classify crop diseases. Transfer learning with full connected layer are used, to shorten and decrease the training time and images needed. The dataset used for the experiments is from online plant disease database which is Plant Village Dataset. 210 images of tomato leaves are used in this research. The precision, recall, accuracy and F1-score are calculated for performance evaluation. The result show that Resnet50 perform the best compared to the other deep learning models with accuracy of 92%. |
| first_indexed | 2024-03-05T21:30:58Z |
| format | Article |
| id | utm.eprints-104574 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:30:58Z |
| publishDate | 2022 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | utm.eprints-1045742024-02-14T06:07:12Z http://eprints.utm.my/104574/ A review of agriculture crop diseases detection using deep learning. Mohd. Anuar, Mohd. Syahid Kadir, Muhammad Solihin Q Science (General) QK Botany Crop diseases has been causing a lot of loss in agriculture sector. The fast and accurate diagnosis of crop diseases is crucial in preventing and limiting loss from the crop diseases. To achieve this goal, method such as deep learning can be used to detect crop diseases. In this study, we review and study the performance of three convolutional neural network model, which is VGG16, VGG19 and Resnet50 model to classify crop diseases. Transfer learning with full connected layer are used, to shorten and decrease the training time and images needed. The dataset used for the experiments is from online plant disease database which is Plant Village Dataset. 210 images of tomato leaves are used in this research. The precision, recall, accuracy and F1-score are calculated for performance evaluation. The result show that Resnet50 perform the best compared to the other deep learning models with accuracy of 92%. Penerbit UTM Press 2022-05-22 Article PeerReviewed application/pdf en http://eprints.utm.my/104574/1/MohammadSolihinKadirSyahidAnuar2022_AReviewofAgricultureCropDiseasesDetection.pdf Mohd. Anuar, Mohd. Syahid and Kadir, Muhammad Solihin (2022) A review of agriculture crop diseases detection using deep learning. Open International Journal Of Informatics, 10 (1). pp. 87-97. ISSN 2289-2370 https://oiji.utm.my/index.php/oiji/article/view/184/137 NA |
| spellingShingle | Q Science (General) QK Botany Mohd. Anuar, Mohd. Syahid Kadir, Muhammad Solihin A review of agriculture crop diseases detection using deep learning. |
| title | A review of agriculture crop diseases detection using deep learning. |
| title_full | A review of agriculture crop diseases detection using deep learning. |
| title_fullStr | A review of agriculture crop diseases detection using deep learning. |
| title_full_unstemmed | A review of agriculture crop diseases detection using deep learning. |
| title_short | A review of agriculture crop diseases detection using deep learning. |
| title_sort | review of agriculture crop diseases detection using deep learning |
| topic | Q Science (General) QK Botany |
| url | http://eprints.utm.my/104574/1/MohammadSolihinKadirSyahidAnuar2022_AReviewofAgricultureCropDiseasesDetection.pdf |
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