Rubber plant disease detection using hybrid fuzzy neural network techniques
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| Format: | Thesis |
| Language: | English |
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2024
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| Online Access: | https://ir.upsi.edu.my/detailsg.php?det=13320 |
| Abstract | Abstract here |
| _version_ | 1855626376729591808 |
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| author | Rizqi Elmuna Hidayah |
| author_facet | Rizqi Elmuna Hidayah |
| author_sort | Rizqi Elmuna Hidayah |
| description | |
| format | Thesis |
| id | upsi-13320 |
| institution | Universiti Pendidikan Sultan Idris |
| language | English |
| publishDate | 2024 |
| record_format | sWADAH |
| record_pdf | Restricted |
| spelling | upsi-133202025-09-24 Rubber plant disease detection using hybrid fuzzy neural network techniques 2024 Rizqi Elmuna Hidayah TK Electrical engineering. Electronics Nuclear engineering <p>Early disease detection in rubber plants (Hevea brasiliensis) is challenging, requiring expert knowledge and experience to confirm diseases, which is time-consuming and costly. Therefore, this study aims to develop a disease detection and prediction system using image processing techniques and artificial intelligence methods. Four types of diseases were identified: three leaf diseases (Oidium powdery mildew, Corynespora, and Collectotrichum) and one root disease (white root disease) with three stages (light, moderate, and severe). Samples were collected from rubber plantations in Tabalong, South Kalimantan, totaling 450 images. The dataset was modeled based on expert labeling. GLCM was used for texture extraction, with six selected features: contrast, correlation, energy, homogeneity, entropy, and inverse difference moment. The utilization of ANFIS and RBFNN provides a powerful and flexible approach to plant disease detection. These methods learn from training data and adjust their parameters to enhance model performance. The accuracy of detecting leaf diseases was 97.78%, with a precision of 0.98, a recall of 0.98, and an F-measure of 0.98. These results were obtained using an epoch value of 40 and value 2, with the gbell type used for the membership function. Similarly, the accuracy for detecting white root disease was 86.67%, with a precision of 0.87, a recall of 0.87, and an F-measure of 0.86. The results indicate that the choice of image processing technique significantly impacts the detection outcome. The effectiveness of the ANFIS classification technique depends on the parameter values selected, including the number of epochs and the number and type of membership functions. This capacity to generalize from training data to new, unseen data is of significant importance for real-world applications. The developed automated system greatly assists farmers in detecting rubber plant diseases, enabling prompt identification and treatment, which in turn reduces operational costs.</p> 2024 thesis https://ir.upsi.edu.my/detailsg.php?det=13320 https://ir.upsi.edu.my/detailsg.php?det=13320 text eng - openAccess Doctoral Perpustakaan Tuanku Bainun Fakulti Komputeran dan META-Teknologi <p>Abdullah, N. E., Rahim, A. A., Hashim, H., & Kamal, M. M. (2007). 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| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Rizqi Elmuna Hidayah Rubber plant disease detection using hybrid fuzzy neural network techniques |
| thesis_level | PhD |
| title | Rubber plant disease detection using hybrid fuzzy neural network techniques |
| title_full | Rubber plant disease detection using hybrid fuzzy neural network techniques |
| title_fullStr | Rubber plant disease detection using hybrid fuzzy neural network techniques |
| title_full_unstemmed | Rubber plant disease detection using hybrid fuzzy neural network techniques |
| title_short | Rubber plant disease detection using hybrid fuzzy neural network techniques |
| title_sort | rubber plant disease detection using hybrid fuzzy neural network techniques |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | https://ir.upsi.edu.my/detailsg.php?det=13320 |
| work_keys_str_mv | AT rizqielmunahidayah rubberplantdiseasedetectionusinghybridfuzzyneuralnetworktechniques |