Analysis for Detecting Banana Leaf Disease Using the CNN Method
Banana farmers face major challenges due to banana leaf diseases such as Cordana, Pestalotiopsis and Sigatoka, which severely affect the quality and quantity of the crop. Early detection of these diseases is particularly challenging as the initial symptoms are often similar to other disorders. To so...
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| Format: | Article |
| Language: | Indonesian |
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Universitas Muhammadiyah Purwokerto
2025-03-01
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| Series: | Jurnal Informatika |
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| Online Access: | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/24514 |
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| author | Nita Helmawati Ema Utami |
| author_facet | Nita Helmawati Ema Utami |
| author_sort | Nita Helmawati |
| collection | DOAJ |
| description | Banana farmers face major challenges due to banana leaf diseases such as Cordana, Pestalotiopsis and Sigatoka, which severely affect the quality and quantity of the crop. Early detection of these diseases is particularly challenging as the initial symptoms are often similar to other disorders. To solve this problem, fast and accurate automated detection is needed to help farmers effectively identify diseases on banana leaves. This research focuses on developing a banana leaf disease detection model using Convolutional Neural Network (CNN) method with MobileNetV2 architecture. The dataset used consists of 937 images of both infected and healthy banana leaves. These images were collected under various lighting conditions and viewing angles to simulate real field situations. The dataset was divided into 70% for training, 20% for validation, and 10% for testing, to ensure robust model evaluation. The CNN model was trained to recognize important visual features on banana leaves that indicate disease infection. The results showed that the model was able to detect banana leaf diseases with an accuracy of 90.62%, indicating high effectiveness. This accuracy confirms the potential of CNN in significantly improving the disease detection process on banana plants. This research is expected to help farmers identify diseases more quickly and accurately, thereby minimizing yield losses and increasing productivity. In addition, this research provides valuable insights into the application of technology in agriculture, particularly in plant disease detection which opens up opportunities for further advancements in this sector. |
| format | Article |
| id | doaj-art-00c5edc396c84e11a5cd6e2d883c36ea |
| institution | Kabale University |
| issn | 2086-9398 2579-8901 |
| language | Indonesian |
| publishDate | 2025-03-01 |
| publisher | Universitas Muhammadiyah Purwokerto |
| record_format | Article |
| series | Jurnal Informatika |
| spelling | doaj-art-00c5edc396c84e11a5cd6e2d883c36ea2025-08-20T03:39:36ZindUniversitas Muhammadiyah PurwokertoJurnal Informatika2086-93982579-89012025-03-01293610.30595/juita.v13i1.2451419794Analysis for Detecting Banana Leaf Disease Using the CNN MethodNita Helmawati0Ema Utami1Universitas Amikom YogyakartaUniversitas Amikom YogyakartaBanana farmers face major challenges due to banana leaf diseases such as Cordana, Pestalotiopsis and Sigatoka, which severely affect the quality and quantity of the crop. Early detection of these diseases is particularly challenging as the initial symptoms are often similar to other disorders. To solve this problem, fast and accurate automated detection is needed to help farmers effectively identify diseases on banana leaves. This research focuses on developing a banana leaf disease detection model using Convolutional Neural Network (CNN) method with MobileNetV2 architecture. The dataset used consists of 937 images of both infected and healthy banana leaves. These images were collected under various lighting conditions and viewing angles to simulate real field situations. The dataset was divided into 70% for training, 20% for validation, and 10% for testing, to ensure robust model evaluation. The CNN model was trained to recognize important visual features on banana leaves that indicate disease infection. The results showed that the model was able to detect banana leaf diseases with an accuracy of 90.62%, indicating high effectiveness. This accuracy confirms the potential of CNN in significantly improving the disease detection process on banana plants. This research is expected to help farmers identify diseases more quickly and accurately, thereby minimizing yield losses and increasing productivity. In addition, this research provides valuable insights into the application of technology in agriculture, particularly in plant disease detection which opens up opportunities for further advancements in this sector.http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/24514agricultural sectorbanana leaf disease detectioncnn model. |
| spellingShingle | Nita Helmawati Ema Utami Analysis for Detecting Banana Leaf Disease Using the CNN Method Jurnal Informatika agricultural sector banana leaf disease detection cnn model. |
| title | Analysis for Detecting Banana Leaf Disease Using the CNN Method |
| title_full | Analysis for Detecting Banana Leaf Disease Using the CNN Method |
| title_fullStr | Analysis for Detecting Banana Leaf Disease Using the CNN Method |
| title_full_unstemmed | Analysis for Detecting Banana Leaf Disease Using the CNN Method |
| title_short | Analysis for Detecting Banana Leaf Disease Using the CNN Method |
| title_sort | analysis for detecting banana leaf disease using the cnn method |
| topic | agricultural sector banana leaf disease detection cnn model. |
| url | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/24514 |
| work_keys_str_mv | AT nitahelmawati analysisfordetectingbananaleafdiseaseusingthecnnmethod AT emautami analysisfordetectingbananaleafdiseaseusingthecnnmethod |