IDENTIFIKASI GEJALA PENYAKIT TANAMAN JERUK MENGGUNAKAN FAST FOURIER TRANSFORM DAN LOCAL BINARY PATTERN DENGAN PROBABILISTIC NEURAL NETWORK
Processing characteristics of digital objects as the basis identification of the digital image, involves knowledge as the interpretation of visual information by using related methods to be implemented. Diseases in plants commonly shows symptoms by the presence of the spot or not, and discoloration....
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| Format: | Thesis |
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[Yogyakarta] : Universitas Gadjah Mada
2014
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| author | , Maura Widyaningsih , Drs. Agus Harjoko, M. Sc., Ph. D. |
| author_facet | , Maura Widyaningsih , Drs. Agus Harjoko, M. Sc., Ph. D. |
| author_sort | , Maura Widyaningsih |
| collection | UGM |
| description | Processing characteristics of digital objects as the basis identification of the digital image, involves knowledge as the interpretation of visual information by using related methods to be implemented. Diseases in plants commonly shows symptoms by the presence of the spot or not, and discoloration. The similarity between the image colors can occur with one another, then the texture identifier could used in these study.
The system constructed in this study as a solution for the identification of disease in citrus plants through image processing, involved methods and concepts. Stages of the process undertaken were preprocessing, feature extraction, and identification. Preprocessing method to resize, clipping, texture with usharp mask sharpening filter with kernel Gausian used and conversion of RGB to gray. Feature Extraction with Fast Fourier Transform (FFT) and Local Binary Pattern (LBP) uniform rotation invariant. FFT is a fast extraction of the Fourier transformation, while LBPP,Rriu2 a feature extraction descriptions pattern on the gray image. The process of identification with Probabilistic methods Neural Network (PNN) used determine of the accuracy of the test results.
Test results with PNN were 233 data, devided into 157 as training data, and 76 as test data. The highest accuracy results on stem, leaves, and fruit of training data showed an average of 100%. Accuracy on test bars obtained 52.94% with a FFT feature extraction, and the accuracy of 34.29% on the test leaves three feature extraction namely FFT, LBPP,Rriu2, and a combination of both. The accuracy of the test fruit 58.33 % with a FFT feature extraction. |
| first_indexed | 2024-03-13T23:37:40Z |
| format | Thesis |
| id | oai:generic.eprints.org:132909 |
| institution | Universiti Gadjah Mada |
| last_indexed | 2024-03-13T23:37:40Z |
| publishDate | 2014 |
| publisher | [Yogyakarta] : Universitas Gadjah Mada |
| record_format | dspace |
| spelling | oai:generic.eprints.org:1329092016-03-04T08:02:03Z https://repository.ugm.ac.id/132909/ IDENTIFIKASI GEJALA PENYAKIT TANAMAN JERUK MENGGUNAKAN FAST FOURIER TRANSFORM DAN LOCAL BINARY PATTERN DENGAN PROBABILISTIC NEURAL NETWORK , Maura Widyaningsih , Drs. Agus Harjoko, M. Sc., Ph. D. ETD Processing characteristics of digital objects as the basis identification of the digital image, involves knowledge as the interpretation of visual information by using related methods to be implemented. Diseases in plants commonly shows symptoms by the presence of the spot or not, and discoloration. The similarity between the image colors can occur with one another, then the texture identifier could used in these study. The system constructed in this study as a solution for the identification of disease in citrus plants through image processing, involved methods and concepts. Stages of the process undertaken were preprocessing, feature extraction, and identification. Preprocessing method to resize, clipping, texture with usharp mask sharpening filter with kernel Gausian used and conversion of RGB to gray. Feature Extraction with Fast Fourier Transform (FFT) and Local Binary Pattern (LBP) uniform rotation invariant. FFT is a fast extraction of the Fourier transformation, while LBPP,Rriu2 a feature extraction descriptions pattern on the gray image. The process of identification with Probabilistic methods Neural Network (PNN) used determine of the accuracy of the test results. Test results with PNN were 233 data, devided into 157 as training data, and 76 as test data. The highest accuracy results on stem, leaves, and fruit of training data showed an average of 100%. Accuracy on test bars obtained 52.94% with a FFT feature extraction, and the accuracy of 34.29% on the test leaves three feature extraction namely FFT, LBPP,Rriu2, and a combination of both. The accuracy of the test fruit 58.33 % with a FFT feature extraction. [Yogyakarta] : Universitas Gadjah Mada 2014 Thesis NonPeerReviewed , Maura Widyaningsih and , Drs. Agus Harjoko, M. Sc., Ph. D. (2014) IDENTIFIKASI GEJALA PENYAKIT TANAMAN JERUK MENGGUNAKAN FAST FOURIER TRANSFORM DAN LOCAL BINARY PATTERN DENGAN PROBABILISTIC NEURAL NETWORK. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=73454 |
| spellingShingle | ETD , Maura Widyaningsih , Drs. Agus Harjoko, M. Sc., Ph. D. IDENTIFIKASI GEJALA PENYAKIT TANAMAN JERUK MENGGUNAKAN FAST FOURIER TRANSFORM DAN LOCAL BINARY PATTERN DENGAN PROBABILISTIC NEURAL NETWORK |
| title | IDENTIFIKASI GEJALA PENYAKIT TANAMAN JERUK MENGGUNAKAN FAST FOURIER TRANSFORM DAN LOCAL BINARY PATTERN DENGAN PROBABILISTIC NEURAL NETWORK |
| title_full | IDENTIFIKASI GEJALA PENYAKIT TANAMAN JERUK MENGGUNAKAN FAST FOURIER TRANSFORM DAN LOCAL BINARY PATTERN DENGAN PROBABILISTIC NEURAL NETWORK |
| title_fullStr | IDENTIFIKASI GEJALA PENYAKIT TANAMAN JERUK MENGGUNAKAN FAST FOURIER TRANSFORM DAN LOCAL BINARY PATTERN DENGAN PROBABILISTIC NEURAL NETWORK |
| title_full_unstemmed | IDENTIFIKASI GEJALA PENYAKIT TANAMAN JERUK MENGGUNAKAN FAST FOURIER TRANSFORM DAN LOCAL BINARY PATTERN DENGAN PROBABILISTIC NEURAL NETWORK |
| title_short | IDENTIFIKASI GEJALA PENYAKIT TANAMAN JERUK MENGGUNAKAN FAST FOURIER TRANSFORM DAN LOCAL BINARY PATTERN DENGAN PROBABILISTIC NEURAL NETWORK |
| title_sort | identifikasi gejala penyakit tanaman jeruk menggunakan fast fourier transform dan local binary pattern dengan probabilistic neural network |
| topic | ETD |
| work_keys_str_mv | AT maurawidyaningsih identifikasigejalapenyakittanamanjerukmenggunakanfastfouriertransformdanlocalbinarypatterndenganprobabilisticneuralnetwork AT drsagusharjokomscphd identifikasigejalapenyakittanamanjerukmenggunakanfastfouriertransformdanlocalbinarypatterndenganprobabilisticneuralnetwork |