Analysis of Lime Leaf Disease using Deep Learning
Currently, lime is a type of plant that has been cultivated in many places. Because limes are used in cooking, and their properties are herb. In Thailand, limes are used for drinking and medical herb to used in health care. From this popularity, the cultivation of limes became widespread and began t...
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| Format: | Article |
| Language: | English |
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Faculty of Informatics
2022-05-01
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| Series: | วารสารวิทยาการสารสนเทศและเทคโนโลยีประยุกต์ |
| Subjects: | |
| Online Access: | https://ph01.tci-thaijo.org/index.php/jait/article/view/248021 |
| _version_ | 1797848515811999744 |
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| author | Umaporn Saisangchan Rapeeporn Chamchong Atthapol Suwannasa |
| author_facet | Umaporn Saisangchan Rapeeporn Chamchong Atthapol Suwannasa |
| author_sort | Umaporn Saisangchan |
| collection | DOAJ |
| description | Currently, lime is a type of plant that has been cultivated in many places. Because limes are used in cooking, and their properties are herb. In Thailand, limes are used for drinking and medical herb to used in health care. From this popularity, the cultivation of limes became widespread and began to be planted more in farm and more at home. Nowadays, lime cultivation can be controlled to produce off-season produce. However, lime is also prone to diseases without proper care. For these reasons, this research is to study the analyzation of decease from lime leaves by using deep learning. The Convolutional Neural Networks (CNNs) of deep learning is used to classify the decease. The proposed architecture of CNNs in this study is to compare to LeNet-5, VGG16, and RestNet-50 architectures. The total number of single lime leaf images is 5,710. The input images are RGB color. The normal and decease lime leaves are separated in equal. Training and test sets are 80 and 20 percent, respectively. The evaluation result is found that LeNet-5 has the lowest accuracy, while the proposed architecture has the highest accuracy but it is not different from ResNet-50. |
| first_indexed | 2024-04-09T18:28:53Z |
| format | Article |
| id | doaj.art-91cf3472e71446edb0cf4244d0a33026 |
| institution | Directory Open Access Journal |
| issn | 2630-094X 2586-8136 |
| language | English |
| last_indexed | 2024-04-09T18:28:53Z |
| publishDate | 2022-05-01 |
| publisher | Faculty of Informatics |
| record_format | Article |
| series | วารสารวิทยาการสารสนเทศและเทคโนโลยีประยุกต์ |
| spelling | doaj.art-91cf3472e71446edb0cf4244d0a330262023-04-11T17:30:31ZengFaculty of Informaticsวารสารวิทยาการสารสนเทศและเทคโนโลยีประยุกต์2630-094X2586-81362022-05-0141718610.14456/jait.2022.6Analysis of Lime Leaf Disease using Deep LearningUmaporn Saisangchan0Rapeeporn Chamchong1Atthapol Suwannasa2Polar Laboratory, Department of Computer Science, Faculty of Informatics, Mahasarakham University, Mahasarakham 44150, ThailandPolar Laboratory, Department of Computer Science, Faculty of Informatics, Mahasarakham University, Mahasarakham 44150, ThailandInformation Security and Advanced Network (ISAN), Department of Computer Science, Faculty of Informatics, Mahasarakham University, Mahasarakham 44150, ThailandCurrently, lime is a type of plant that has been cultivated in many places. Because limes are used in cooking, and their properties are herb. In Thailand, limes are used for drinking and medical herb to used in health care. From this popularity, the cultivation of limes became widespread and began to be planted more in farm and more at home. Nowadays, lime cultivation can be controlled to produce off-season produce. However, lime is also prone to diseases without proper care. For these reasons, this research is to study the analyzation of decease from lime leaves by using deep learning. The Convolutional Neural Networks (CNNs) of deep learning is used to classify the decease. The proposed architecture of CNNs in this study is to compare to LeNet-5, VGG16, and RestNet-50 architectures. The total number of single lime leaf images is 5,710. The input images are RGB color. The normal and decease lime leaves are separated in equal. Training and test sets are 80 and 20 percent, respectively. The evaluation result is found that LeNet-5 has the lowest accuracy, while the proposed architecture has the highest accuracy but it is not different from ResNet-50.https://ph01.tci-thaijo.org/index.php/jait/article/view/248021deep learningconvolutional neural networkclassificationplant disease |
| spellingShingle | Umaporn Saisangchan Rapeeporn Chamchong Atthapol Suwannasa Analysis of Lime Leaf Disease using Deep Learning วารสารวิทยาการสารสนเทศและเทคโนโลยีประยุกต์ deep learning convolutional neural network classification plant disease |
| title | Analysis of Lime Leaf Disease using Deep Learning |
| title_full | Analysis of Lime Leaf Disease using Deep Learning |
| title_fullStr | Analysis of Lime Leaf Disease using Deep Learning |
| title_full_unstemmed | Analysis of Lime Leaf Disease using Deep Learning |
| title_short | Analysis of Lime Leaf Disease using Deep Learning |
| title_sort | analysis of lime leaf disease using deep learning |
| topic | deep learning convolutional neural network classification plant disease |
| url | https://ph01.tci-thaijo.org/index.php/jait/article/view/248021 |
| work_keys_str_mv | AT umapornsaisangchan analysisoflimeleafdiseaseusingdeeplearning AT rapeepornchamchong analysisoflimeleafdiseaseusingdeeplearning AT atthapolsuwannasa analysisoflimeleafdiseaseusingdeeplearning |