Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture
In the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional neural networks (CNN) are rapidly advancing, making it...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2023-02-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4622 |
| _version_ | 1797327713363558400 |
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| author | Yazid Aufar Muhammad Helmy Abdillah Jiki Romadoni |
| author_facet | Yazid Aufar Muhammad Helmy Abdillah Jiki Romadoni |
| author_sort | Yazid Aufar |
| collection | DOAJ |
| description | In the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional neural networks (CNN) are rapidly advancing, making it possible to diagnose Arabica coffee leaf damage without a specialist's help. CNN is aimed to find features adaptively through backpropagation by adding layers including convolutional layers and pooling layers. This study aims to optimize and increase the accuracy of Arabica coffee leaf disease classification utilizing the neural network architectures: ResNet50, InceptionResNetV4, MobileNetV2, and DensNet169. Additionally, this research presents an interactive web platform integrated with the Arabica coffee leaf disease prediction system. Inside this research, 5000 image data points will be divided into five classes—Phoma, Rust, Cescospora, healthy, and Miner—to assess the efficacy of CNN architecture in classifying images of Arabica coffee leaf disease. 80:10:10 is the ratio between training data, validation, and testing. In the testing findings, the InceptionResnetV2 and DensNet169 designs had the highest accuracy, at 100%, followed by the MobileNetV2 architecture at 99% and the ResNet50 architecture at 59%. Even though MobileNetV2 is not more accurate than InceptionResnetV2 and DensNet169, MobileNetV2 is the smallest of the three models. The MobileNetV2 paradigm was chosen for web application development. The system accurately identified and advised treatment for Arabica coffee leaf diseases, as shown by the system's implementation outcomes. |
| first_indexed | 2024-03-08T06:42:27Z |
| format | Article |
| id | doaj.art-a897de84b4dd4fa0a3629c3db2a05836 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T06:42:27Z |
| publishDate | 2023-02-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-a897de84b4dd4fa0a3629c3db2a058362024-02-03T08:31:44ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602023-02-0171717910.29207/resti.v7i1.46224622Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart AgricultureYazid Aufar0Muhammad Helmy Abdillah1Jiki Romadoni2Politeknik HasnurPoliteknik HasnurPoliteknik HasnurIn the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional neural networks (CNN) are rapidly advancing, making it possible to diagnose Arabica coffee leaf damage without a specialist's help. CNN is aimed to find features adaptively through backpropagation by adding layers including convolutional layers and pooling layers. This study aims to optimize and increase the accuracy of Arabica coffee leaf disease classification utilizing the neural network architectures: ResNet50, InceptionResNetV4, MobileNetV2, and DensNet169. Additionally, this research presents an interactive web platform integrated with the Arabica coffee leaf disease prediction system. Inside this research, 5000 image data points will be divided into five classes—Phoma, Rust, Cescospora, healthy, and Miner—to assess the efficacy of CNN architecture in classifying images of Arabica coffee leaf disease. 80:10:10 is the ratio between training data, validation, and testing. In the testing findings, the InceptionResnetV2 and DensNet169 designs had the highest accuracy, at 100%, followed by the MobileNetV2 architecture at 99% and the ResNet50 architecture at 59%. Even though MobileNetV2 is not more accurate than InceptionResnetV2 and DensNet169, MobileNetV2 is the smallest of the three models. The MobileNetV2 paradigm was chosen for web application development. The system accurately identified and advised treatment for Arabica coffee leaf diseases, as shown by the system's implementation outcomes.http://jurnal.iaii.or.id/index.php/RESTI/article/view/4622arabica coffeeconvolutional neural networksimage processingleaf diseasemachine learning |
| spellingShingle | Yazid Aufar Muhammad Helmy Abdillah Jiki Romadoni Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) arabica coffee convolutional neural networks image processing leaf disease machine learning |
| title | Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture |
| title_full | Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture |
| title_fullStr | Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture |
| title_full_unstemmed | Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture |
| title_short | Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture |
| title_sort | web based cnn application for arabica coffee leaf disease prediction in smart agriculture |
| topic | arabica coffee convolutional neural networks image processing leaf disease machine learning |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4622 |
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