Exploring CycleGAN Technique for Improved Plant Disease Detection and Analysis
The threat of plant disease is a significant problem affecting the world, when untreated these diseases can affect food production. Diagnosis of these diseases in an un-delayed manner is very important, however, methods described in current use that only involve the use of sight are inefficient and...
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| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03017.pdf |
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| _version_ | 1825206586484719616 |
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| author | Ouyang Luyi |
| author_facet | Ouyang Luyi |
| author_sort | Ouyang Luyi |
| collection | DOAJ |
| description | The threat of plant disease is a significant problem affecting the world, when untreated these diseases can affect food production. Diagnosis of these diseases in an un-delayed manner is very important, however, methods described in current use that only involve the use of sight are inefficient and are also subject to errors. This paper tackles the problem by using Cycle-Consistent General Adversarial Networks (CycleGAN) to create artificial images of diseased plant leaves. The advantage of this approach is that augmenting the training data with images that do not exist in the real world helps improve the performance of disease classifications. The research takes into consideration the apple leaves diseased images, is of various pathogens, and CycleGAN creates images to even it. The results indicate that CycleGAN is indeed able to generate artificial images for the less complicated sicknesses associated with a mere shift in color, with an achieved micro-average Area Under the Curve (AUC) of .98 and macro-average AUC of 0.94. On the contrary, this model has problems in striking a balance while dealing with more complex diseases that have problems that are underlying structural deformation. However, adding such images in training datasets increases the classification accuracy in total. Future work should involve making the model more robust to complex and rich visual details as well as employing more sophisticated models for better applicability in real farming settings. |
| format | Article |
| id | doaj-art-affebf0441974d598ebac7be4f8f8c57 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-affebf0441974d598ebac7be4f8f8c572025-02-07T08:21:12ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700301710.1051/itmconf/20257003017itmconf_dai2024_03017Exploring CycleGAN Technique for Improved Plant Disease Detection and AnalysisOuyang Luyi0Olin Business School, Washington University in St. LouisThe threat of plant disease is a significant problem affecting the world, when untreated these diseases can affect food production. Diagnosis of these diseases in an un-delayed manner is very important, however, methods described in current use that only involve the use of sight are inefficient and are also subject to errors. This paper tackles the problem by using Cycle-Consistent General Adversarial Networks (CycleGAN) to create artificial images of diseased plant leaves. The advantage of this approach is that augmenting the training data with images that do not exist in the real world helps improve the performance of disease classifications. The research takes into consideration the apple leaves diseased images, is of various pathogens, and CycleGAN creates images to even it. The results indicate that CycleGAN is indeed able to generate artificial images for the less complicated sicknesses associated with a mere shift in color, with an achieved micro-average Area Under the Curve (AUC) of .98 and macro-average AUC of 0.94. On the contrary, this model has problems in striking a balance while dealing with more complex diseases that have problems that are underlying structural deformation. However, adding such images in training datasets increases the classification accuracy in total. Future work should involve making the model more robust to complex and rich visual details as well as employing more sophisticated models for better applicability in real farming settings.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03017.pdf |
| spellingShingle | Ouyang Luyi Exploring CycleGAN Technique for Improved Plant Disease Detection and Analysis ITM Web of Conferences |
| title | Exploring CycleGAN Technique for Improved Plant Disease Detection and Analysis |
| title_full | Exploring CycleGAN Technique for Improved Plant Disease Detection and Analysis |
| title_fullStr | Exploring CycleGAN Technique for Improved Plant Disease Detection and Analysis |
| title_full_unstemmed | Exploring CycleGAN Technique for Improved Plant Disease Detection and Analysis |
| title_short | Exploring CycleGAN Technique for Improved Plant Disease Detection and Analysis |
| title_sort | exploring cyclegan technique for improved plant disease detection and analysis |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03017.pdf |
| work_keys_str_mv | AT ouyangluyi exploringcyclegantechniqueforimprovedplantdiseasedetectionandanalysis |