Application of Faster R-CNN Deep Learning Method for Rice Plant Disease Detection
Plant diseases, particularly in staple crops like rice, significantly affect the stability of rice production in Indonesia. Crop failure caused by rice plant diseases present a critical challenge for farmers. Early diagnosis is crucial for preventing and managing rice diseases, as it facilitates mo...
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| Format: | Article |
| Language: | English |
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P3M Politeknik Negeri Banjarmasin
2024-12-01
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| Series: | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
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| Online Access: | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1165 |
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| _version_ | 1846106891989549056 |
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| author | Halim Pujiono Anik Vega Vitianingsih Slamet Kacung Anastasia Lidya Maukar Seftin Fitri Ana Wati |
| author_facet | Halim Pujiono Anik Vega Vitianingsih Slamet Kacung Anastasia Lidya Maukar Seftin Fitri Ana Wati |
| author_sort | Halim Pujiono |
| collection | DOAJ |
| description | Plant diseases, particularly in staple crops like rice, significantly affect the stability of rice production in Indonesia. Crop failure caused by rice plant diseases present a critical challenge for farmers. Early diagnosis is crucial for preventing and managing rice diseases, as it facilitates more effective preventive measures, reduces yield losses, and boosts overall agricultural production. This study aims to apply the Faster Region Convolutional Neural Network (Faster R-CNN), a deep learning approach, to detect rice plant diseases. The Grid Search method was employed as a hyperparameter tuning technique to identify the optimal parameter combination for enhancing algorithm performance. Experimental results demonstrate the model's performance, achieving an accuracy rate of 88%, recall and precision of 100%, and an F1 Score of 93%. These findings indicate that the Faster R-CNN method effectively recognizes and classifies rice plant diseases with a high degree of accuracy. |
| format | Article |
| id | doaj-art-f6b931f4088e4e3db6c5668858f1c9a9 |
| institution | Kabale University |
| issn | 2598-3245 2598-3288 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | P3M Politeknik Negeri Banjarmasin |
| record_format | Article |
| series | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
| spelling | doaj-art-f6b931f4088e4e3db6c5668858f1c9a92024-12-27T05:22:24ZengP3M Politeknik Negeri BanjarmasinJurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer2598-32452598-32882024-12-018211111810.31961/eltikom.v8i2.11651121Application of Faster R-CNN Deep Learning Method for Rice Plant Disease DetectionHalim Pujiono0Anik Vega Vitianingsih1Slamet Kacung2Anastasia Lidya Maukar3Seftin Fitri Ana Wati4Universitas Dr. Soetomo, IndonesiaUniversitas Dr.Soetomo, IndonesiaUniversitas Dr. Soetomo, IndonesiaUniversitas Presiden, IndonesiaUPN Veteran Jawa Timur, IndonesiaPlant diseases, particularly in staple crops like rice, significantly affect the stability of rice production in Indonesia. Crop failure caused by rice plant diseases present a critical challenge for farmers. Early diagnosis is crucial for preventing and managing rice diseases, as it facilitates more effective preventive measures, reduces yield losses, and boosts overall agricultural production. This study aims to apply the Faster Region Convolutional Neural Network (Faster R-CNN), a deep learning approach, to detect rice plant diseases. The Grid Search method was employed as a hyperparameter tuning technique to identify the optimal parameter combination for enhancing algorithm performance. Experimental results demonstrate the model's performance, achieving an accuracy rate of 88%, recall and precision of 100%, and an F1 Score of 93%. These findings indicate that the Faster R-CNN method effectively recognizes and classifies rice plant diseases with a high degree of accuracy.https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1165faster r-cnngrid searchdeep learninghyperparameter tuningrice plant disease detection |
| spellingShingle | Halim Pujiono Anik Vega Vitianingsih Slamet Kacung Anastasia Lidya Maukar Seftin Fitri Ana Wati Application of Faster R-CNN Deep Learning Method for Rice Plant Disease Detection Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer faster r-cnn grid search deep learning hyperparameter tuning rice plant disease detection |
| title | Application of Faster R-CNN Deep Learning Method for Rice Plant Disease Detection |
| title_full | Application of Faster R-CNN Deep Learning Method for Rice Plant Disease Detection |
| title_fullStr | Application of Faster R-CNN Deep Learning Method for Rice Plant Disease Detection |
| title_full_unstemmed | Application of Faster R-CNN Deep Learning Method for Rice Plant Disease Detection |
| title_short | Application of Faster R-CNN Deep Learning Method for Rice Plant Disease Detection |
| title_sort | application of faster r cnn deep learning method for rice plant disease detection |
| topic | faster r-cnn grid search deep learning hyperparameter tuning rice plant disease detection |
| url | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1165 |
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