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Transfer Learning VGG16 For Image Classification of Tomato Leaf Disease
Corresponding Author(s) : Muhammad Resa Arif Yudianto
Proceedings Universitas Muhammadiyah Yogyakarta Undergraduate Conference,
Vol. 3 No. 2 (2023): Crafting Innovation for Global Benefit
Abstract
Tomatoes (Solanum lycopersicum) are one of Indonesia's mainstay horticultural commodities that are exported throughout Southeast Asia. However, the export value of tomatoes in 2021 recorded a decrease of 34.07% from 2020. The decline in the quality and quantity of tomatoes is generally caused by bacteria, fungi, viruses, and mite outbreaks that mostly attack the leaves such as late blight and two-spotted spider mite. This research utilizes one of the image processing methods to classify tomato leaf diseases in 3 labels, namely tomato healthy, tomato late blight, and tomato two-spotted spider mite. The image processing algorithm used in this research is Convolutional Neural Network (CNN) which can extract leaf image features in depth through its layer architecture. The VGG16 transfer learning architecture is used in this study because of its simple structure and can be modified by adding a fully connected layer, namely dropout with a value of 0.5 to adjust the model and improve its performance. Green Channel + CLAHE is also applied at the preprocessing stage with an epoch parameter of 30. The dataset used consists of 1,591 images of healthy tomato leaves, 1,909 images of late blight tomato leaves, and 1,676 images of two-spotted spider mite leaves. Two scenarios were conducted on the model, namely the model with callback function and the model without callback function. Based on the training and evaluation of the model that has been carried out, the model with the callback function is able to produce an accuracy value of 99.03% with precision for the labels tomato healthy 0.99, tomato late blight 1.00, and tomato two-spotted spider mite 0.98, and the number of incorrectly predicted images is only 15. This shows a higher value than the model without the callback function. Against 21 test images from other datasets, the model with callback function was able to produce accurate classification with high prediction values.
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- A. Ulfa Martyas. (2021). Pengaruh Pemberian Pupuk Kotoran Kambing Terhadap Pertumbuhan Tanaman Tomat (Solanum lycopersicum). Repository UIN Sultan Thaha Saifuddin. http://repository.uinjambi.ac.id/id/eprint/10632
- Al-gaashani, M. S. A. M., Shang, F., Muthanna, M. S. A., Khayyat, M., & Abd El-Latif, A. A. (2022). Tomato leaf disease classification by exploiting transfer learning and feature concatenation. IET Image Processing, 16(3), 913–925. https://doi.org/10.1049/ipr2.12397
- Apriyadi, Z., Liestiany, E., & Rodinah. (2019). Proteksi Tanaman Tropika 2(02):1 Juni 2019 Pengendalian Biologi Penyakit Layu Bakteri (Ralstonia solanacearum) Pada Tanaman Tomat (Lycopersicon esculentum). Jurnal Proteksi Tanaman Tropika, 2(02), 108–114. http://103.81.100.242/index.php/jpt/article/view/149
- Astiningrum, M., Prima Arhandi, P., Aqmarina Ariditya, N., Teknologi Informasi, J., & Negeri Malang, P. (2020). JIP (Jurnal Informatika Polinema) Identifikasi Penyakit Pada Daun Tomat Berdasarkan Fitur Warna Dan Tekstur. 6(2), 47–50. http://jurnalti.polinema.ac.id/index.php/SIAP/article/view/496
- BPS RI/BPS-Statistics Indonesia. (2021). Statistik Hortikultura 2021 (Direktorat Statistik Tanaman Pangan Hortikultura dan Perkebunan, Ed.). BPS RI/BPS-Statistics Indonesia. https://www.bps.go.id/publication/2022/06/08/44e935e8c141bcb37569aed3/statistik-hortikultura-2021.html
- Chicco, D., Tötsch, N., & Jurman, G. (2021). The matthews correlation coefficient (Mcc) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Mining, 14, 1–22. https://doi.org/10.1186/s13040-021-00244-z
- Deep, S., & Zheng, X. (2019). Leveraging CNN and Transfer Learning for Vision-based Human Activity Recognition. 2019 29th International Telecommunication Networks and Applications Conference (ITNAC), 1–4. 10.1109/ITNAC46935.2019.9078016
- Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255. 10.1109/CVPR.2009.5206848.
- Desiani, A., Alwine, D., #2, Z., Primartha, R., #4, F. E., Avisa, N., Andriani, C., Masjid, J., Gazali, A., Lama, B., Palembang, K., & Selatan, S. (2021). Variasi Thresholding untuk Segmentasi Pembuluh Darah Citra Retina. JEPIN (Jurnal Edukasi Dan Penelitian Nasional), 7(2), 255–262. http://dx.doi.org/10.26418/jp.v7i2.47205
- Ekananda, N. P., & Riminarsih, D. (2022). Identifikasi Penyakit Pneumonia Berdasarkan Citra Chest X-Ray Menggunakan Convolutional Neural Network. Jurnal Ilmiah Informatika Komputer, 27(1), 79–94. https://doi.org/10.35760/ik.2022.v27i1.6487
- Fadli Gunardi, M. (2022). Implementasi Augmentasi Citra pada Suatu Dataset. https://informatika.stei.itb.ac.id/~rinaldi.munir/Citra/2022-2023/Makalah/Makalah-IF4073-Citra-Sem1-2022%20(24).pdf
- Fasulo, T. R., & Denmark, H. A. (2016). Twospotted Spider Mite, Tetranychus urticae Koch (Arachnida: Acari: Tetranychidae) 1. https://journals.flvc.org/edis/article/download/109059/104197
- Ghojogh, B., & Crowley, M. (2019). The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial. http://arxiv.org/abs/1905.12787
- Gholamy, A., Kreinovich, V., & Kosheleva, O. (2018). A Pedagogical Explanation A Pedagogical Explanation Part of the Computer Sciences Commons. https://scholarworks.utep.edu/cs_techrephttps://scholarworks.utep.edu/cs_techrep/1209
- H. Semangun. (1994). Penyakit Penyakit Tanaman Hortikultura. Gadjah Mada University Press. https://www.academia.edu/40830883/Penyakit_Penyakit_Tanaman_Hortikultura_Di_Indonesia_Haryono_Semangun_.
- Jiang, Z. (2019). A Novel Crop Weed Recognition Method Based on Transfer Learning from VGG16 Implemented by Keras. IOP Conference Series: Materials Science and Engineering, 677(3). https://doi.org/10.1088/1757-899X/677/3/032073
- K. Mahesh Babu. (2022). Tomato Leaf Disease. Kaggle.Com. https://www.kaggle.com/datasets/kmaheshbabu/tomato-leaf-diseases
- Kandel, I., & Castelli, M. (2020). Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Applied Sciences (Switzerland), 10(6). https://doi.org/10.3390/app10062021
- Kanditami, F., Saipudin, D., & Rizal, A. (2014). Analisis Contrast Limited Adaptive Histogram Equalization (CLAHE) Dan Region Growing Dalam Deteksi Gejala Kanker Payudara Pada Citra Mammogram. Jurnal Elektro, 11(2). https://staff.telkomuniversity.ac.id/wp-content/uploads/sites/11/2013/12/jurnal235_Freysenita_elektro_2014_akhir.pdf
- Kemble, J., Bertucci, M., Jennings, K., Rodrigues, C., Walgenbach, J., Wszelaki, A., Sebel Associate Publisher, G., Olwell Western Account Manager, C., & Kloosterman Associate Editor, S. (2022). 2022 Vegetable Crop Handbook for Southeastern United States Great American Media Services. www.vegcrophandbook.com
- Krishnaswamy Rangarajan, A., & Purushothaman, R. (2020). Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-59108-x
- L. Sahrani. (2021). Klasifikasi Penyakit Daun Tomat Berdasarkan Ekstraksi Tekstur Daun Menggunakan Gabor Filter Dan Algoritma Support Vector Machine. http://repository.uinsu.ac.id/13431/
- Lu, J., Tan, L., & Jiang, H. (2021). Review on convolutional neural network (CNN) applied to plant leaf disease classification. In Agriculture (Switzerland) (Vol. 11, Issue 8). MDPI AG. https://doi.org/10.3390/agriculture11080707
- Lumute Unihehu, A., & Suharjo, I. (2021). Klasifikasi Jenis Ikan Berbasis Jaringan Saraf Tiruan Menggunakan Algoritma Principal Component Analysis (PCA). Jurnal Ilmiah Ilmu Komputer, 7(2). https://doi.org/10.35329/jiik.v7i2.200
- Melanson, R. (2020). Common Diseases of Tomatoes. http://extension.msstate.edu/publications/common-diseases-tomatoes
- Mugiyanto, & Nugroho, H. (2000). Budidaya Tomat. Badan Penelitian dan Pengembangan Pertanian Instalasi Penelitian dan Pengkajian Teknologi Pertanian Kota Baru Jambi. http://repository.pertanian.go.id/handle/123456789/14374
- Rasywir, E., Sinaga, R., Pratama, Y., Dinamika, U., & Jambi, B. (2020). Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN). 22(2). https://doi.org/10.31294/p.v21i2
- Rismiyati, & Luthfiarta, A. (2021). VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification. Jurnal Informatika Dan Teknologi Informasi, 18(1), 37–48. https://doi.org/10.31515/telematika.v18i1.4025
- Romir Mehta. (2023). Potato-tomato-strawberry. Kaggle.Com. https://www.kaggle.com/datasets/romirmehta07/potato-tomato-strawberry
- Rozaqi, A. J., Sunyoto, A., & Arief, M. R. (2021). Seminar Nasional & Call Paper Fakultas Sains dan Teknologi (SENASAINS 1 st). Procedia of Engineering and Life Science, 1(1). https://doi.org/10.21070/pels.v1i1.820
- Tan, L., Lu, J., & Jiang, H. (2021). Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods. AgriEngineering, 3(3), 542–558. https://doi.org/10.3390/agriengineering3030035
- Tharwat, A. (2018). Classification assessment methods. Applied Computing and Informatics, 17(1), 168–192. https://doi.org/10.1016/j.aci.2018.08.003
- Uppari, R. R. (2020). Comparison Between KERAS Library and FAST.AI Library using Convolution Neural Network (Image Classification) Model [Dublin Business School]. https://esource.dbs.ie/handle/10788/4249
- Utami, D. A. B. (2021). Perancangan Sistem Login Pada Aplikasi Berbasis GUI Menggunakan QTDesigner Python (Vol. 4, Issue 2). https://jurnal.darmajaya.ac.id/index.php/SIMADA/article/view/2961/1382
- Valentina, R., Rostianingsih, S., & Tjondrowiguno, A. N. (2020). Pengenalan Gambar Botol Plastik dan Kaleng Minuman Menggunakan Metode Convolutional Neural Network.
- Wahid, M. I., Lawi, A., Muh, D. A., & Siddik, A. (2022). Perbandingan Kinerja Model Ensembled Transfer Learning Pada Klasifikasi Penyakit Daun Tomat. Prosiding Seminar Nasional Teknik Elektro Dan Informatika (SNTEI) 2022-Teknik Informatika, 8(1), 286–291. http://118.98.121.208/index.php/sntei/article/view/3630
- Wahid, M. I., Mustamin, S. A., & Lawi, D. A. (2021). Identifikasi Dan Klasifikasi Citra Penyakit Daun Tomat Menggunakan Arsitektur Inception V4. Konferensi Nasional Ilmu Komputer (KONIK) 2021, 2019, 257–264. https://prosiding.konik.id/index.php/konik/article/view/61
- Yustika, A. A., Suhartono, E., Rahmania, R., S1, P., & Telekomunikasi, T. (2019). Deteksi Anemia Melalui Citra Sel Darah Menggunakan Metode Discrete Wavelet Transform (DWT) dan Klasifikasi Support Vector Machine (SVM) Anemia Detection By Means Of Blood Cells Image Using Discrete Wavelet Transform (DWT) And Support Vector Machine (SVM). E-Proceedings of Engineering, 6(2), 3760–3767. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/10123
References
A. Ulfa Martyas. (2021). Pengaruh Pemberian Pupuk Kotoran Kambing Terhadap Pertumbuhan Tanaman Tomat (Solanum lycopersicum). Repository UIN Sultan Thaha Saifuddin. http://repository.uinjambi.ac.id/id/eprint/10632
Al-gaashani, M. S. A. M., Shang, F., Muthanna, M. S. A., Khayyat, M., & Abd El-Latif, A. A. (2022). Tomato leaf disease classification by exploiting transfer learning and feature concatenation. IET Image Processing, 16(3), 913–925. https://doi.org/10.1049/ipr2.12397
Apriyadi, Z., Liestiany, E., & Rodinah. (2019). Proteksi Tanaman Tropika 2(02):1 Juni 2019 Pengendalian Biologi Penyakit Layu Bakteri (Ralstonia solanacearum) Pada Tanaman Tomat (Lycopersicon esculentum). Jurnal Proteksi Tanaman Tropika, 2(02), 108–114. http://103.81.100.242/index.php/jpt/article/view/149
Astiningrum, M., Prima Arhandi, P., Aqmarina Ariditya, N., Teknologi Informasi, J., & Negeri Malang, P. (2020). JIP (Jurnal Informatika Polinema) Identifikasi Penyakit Pada Daun Tomat Berdasarkan Fitur Warna Dan Tekstur. 6(2), 47–50. http://jurnalti.polinema.ac.id/index.php/SIAP/article/view/496
BPS RI/BPS-Statistics Indonesia. (2021). Statistik Hortikultura 2021 (Direktorat Statistik Tanaman Pangan Hortikultura dan Perkebunan, Ed.). BPS RI/BPS-Statistics Indonesia. https://www.bps.go.id/publication/2022/06/08/44e935e8c141bcb37569aed3/statistik-hortikultura-2021.html
Chicco, D., Tötsch, N., & Jurman, G. (2021). The matthews correlation coefficient (Mcc) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Mining, 14, 1–22. https://doi.org/10.1186/s13040-021-00244-z
Deep, S., & Zheng, X. (2019). Leveraging CNN and Transfer Learning for Vision-based Human Activity Recognition. 2019 29th International Telecommunication Networks and Applications Conference (ITNAC), 1–4. 10.1109/ITNAC46935.2019.9078016
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255. 10.1109/CVPR.2009.5206848.
Desiani, A., Alwine, D., #2, Z., Primartha, R., #4, F. E., Avisa, N., Andriani, C., Masjid, J., Gazali, A., Lama, B., Palembang, K., & Selatan, S. (2021). Variasi Thresholding untuk Segmentasi Pembuluh Darah Citra Retina. JEPIN (Jurnal Edukasi Dan Penelitian Nasional), 7(2), 255–262. http://dx.doi.org/10.26418/jp.v7i2.47205
Ekananda, N. P., & Riminarsih, D. (2022). Identifikasi Penyakit Pneumonia Berdasarkan Citra Chest X-Ray Menggunakan Convolutional Neural Network. Jurnal Ilmiah Informatika Komputer, 27(1), 79–94. https://doi.org/10.35760/ik.2022.v27i1.6487
Fadli Gunardi, M. (2022). Implementasi Augmentasi Citra pada Suatu Dataset. https://informatika.stei.itb.ac.id/~rinaldi.munir/Citra/2022-2023/Makalah/Makalah-IF4073-Citra-Sem1-2022%20(24).pdf
Fasulo, T. R., & Denmark, H. A. (2016). Twospotted Spider Mite, Tetranychus urticae Koch (Arachnida: Acari: Tetranychidae) 1. https://journals.flvc.org/edis/article/download/109059/104197
Ghojogh, B., & Crowley, M. (2019). The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial. http://arxiv.org/abs/1905.12787
Gholamy, A., Kreinovich, V., & Kosheleva, O. (2018). A Pedagogical Explanation A Pedagogical Explanation Part of the Computer Sciences Commons. https://scholarworks.utep.edu/cs_techrephttps://scholarworks.utep.edu/cs_techrep/1209
H. Semangun. (1994). Penyakit Penyakit Tanaman Hortikultura. Gadjah Mada University Press. https://www.academia.edu/40830883/Penyakit_Penyakit_Tanaman_Hortikultura_Di_Indonesia_Haryono_Semangun_.
Jiang, Z. (2019). A Novel Crop Weed Recognition Method Based on Transfer Learning from VGG16 Implemented by Keras. IOP Conference Series: Materials Science and Engineering, 677(3). https://doi.org/10.1088/1757-899X/677/3/032073
K. Mahesh Babu. (2022). Tomato Leaf Disease. Kaggle.Com. https://www.kaggle.com/datasets/kmaheshbabu/tomato-leaf-diseases
Kandel, I., & Castelli, M. (2020). Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Applied Sciences (Switzerland), 10(6). https://doi.org/10.3390/app10062021
Kanditami, F., Saipudin, D., & Rizal, A. (2014). Analisis Contrast Limited Adaptive Histogram Equalization (CLAHE) Dan Region Growing Dalam Deteksi Gejala Kanker Payudara Pada Citra Mammogram. Jurnal Elektro, 11(2). https://staff.telkomuniversity.ac.id/wp-content/uploads/sites/11/2013/12/jurnal235_Freysenita_elektro_2014_akhir.pdf
Kemble, J., Bertucci, M., Jennings, K., Rodrigues, C., Walgenbach, J., Wszelaki, A., Sebel Associate Publisher, G., Olwell Western Account Manager, C., & Kloosterman Associate Editor, S. (2022). 2022 Vegetable Crop Handbook for Southeastern United States Great American Media Services. www.vegcrophandbook.com
Krishnaswamy Rangarajan, A., & Purushothaman, R. (2020). Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-59108-x
L. Sahrani. (2021). Klasifikasi Penyakit Daun Tomat Berdasarkan Ekstraksi Tekstur Daun Menggunakan Gabor Filter Dan Algoritma Support Vector Machine. http://repository.uinsu.ac.id/13431/
Lu, J., Tan, L., & Jiang, H. (2021). Review on convolutional neural network (CNN) applied to plant leaf disease classification. In Agriculture (Switzerland) (Vol. 11, Issue 8). MDPI AG. https://doi.org/10.3390/agriculture11080707
Lumute Unihehu, A., & Suharjo, I. (2021). Klasifikasi Jenis Ikan Berbasis Jaringan Saraf Tiruan Menggunakan Algoritma Principal Component Analysis (PCA). Jurnal Ilmiah Ilmu Komputer, 7(2). https://doi.org/10.35329/jiik.v7i2.200
Melanson, R. (2020). Common Diseases of Tomatoes. http://extension.msstate.edu/publications/common-diseases-tomatoes
Mugiyanto, & Nugroho, H. (2000). Budidaya Tomat. Badan Penelitian dan Pengembangan Pertanian Instalasi Penelitian dan Pengkajian Teknologi Pertanian Kota Baru Jambi. http://repository.pertanian.go.id/handle/123456789/14374
Rasywir, E., Sinaga, R., Pratama, Y., Dinamika, U., & Jambi, B. (2020). Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN). 22(2). https://doi.org/10.31294/p.v21i2
Rismiyati, & Luthfiarta, A. (2021). VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification. Jurnal Informatika Dan Teknologi Informasi, 18(1), 37–48. https://doi.org/10.31515/telematika.v18i1.4025
Romir Mehta. (2023). Potato-tomato-strawberry. Kaggle.Com. https://www.kaggle.com/datasets/romirmehta07/potato-tomato-strawberry
Rozaqi, A. J., Sunyoto, A., & Arief, M. R. (2021). Seminar Nasional & Call Paper Fakultas Sains dan Teknologi (SENASAINS 1 st). Procedia of Engineering and Life Science, 1(1). https://doi.org/10.21070/pels.v1i1.820
Tan, L., Lu, J., & Jiang, H. (2021). Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods. AgriEngineering, 3(3), 542–558. https://doi.org/10.3390/agriengineering3030035
Tharwat, A. (2018). Classification assessment methods. Applied Computing and Informatics, 17(1), 168–192. https://doi.org/10.1016/j.aci.2018.08.003
Uppari, R. R. (2020). Comparison Between KERAS Library and FAST.AI Library using Convolution Neural Network (Image Classification) Model [Dublin Business School]. https://esource.dbs.ie/handle/10788/4249
Utami, D. A. B. (2021). Perancangan Sistem Login Pada Aplikasi Berbasis GUI Menggunakan QTDesigner Python (Vol. 4, Issue 2). https://jurnal.darmajaya.ac.id/index.php/SIMADA/article/view/2961/1382
Valentina, R., Rostianingsih, S., & Tjondrowiguno, A. N. (2020). Pengenalan Gambar Botol Plastik dan Kaleng Minuman Menggunakan Metode Convolutional Neural Network.
Wahid, M. I., Lawi, A., Muh, D. A., & Siddik, A. (2022). Perbandingan Kinerja Model Ensembled Transfer Learning Pada Klasifikasi Penyakit Daun Tomat. Prosiding Seminar Nasional Teknik Elektro Dan Informatika (SNTEI) 2022-Teknik Informatika, 8(1), 286–291. http://118.98.121.208/index.php/sntei/article/view/3630
Wahid, M. I., Mustamin, S. A., & Lawi, D. A. (2021). Identifikasi Dan Klasifikasi Citra Penyakit Daun Tomat Menggunakan Arsitektur Inception V4. Konferensi Nasional Ilmu Komputer (KONIK) 2021, 2019, 257–264. https://prosiding.konik.id/index.php/konik/article/view/61
Yustika, A. A., Suhartono, E., Rahmania, R., S1, P., & Telekomunikasi, T. (2019). Deteksi Anemia Melalui Citra Sel Darah Menggunakan Metode Discrete Wavelet Transform (DWT) dan Klasifikasi Support Vector Machine (SVM) Anemia Detection By Means Of Blood Cells Image Using Discrete Wavelet Transform (DWT) And Support Vector Machine (SVM). E-Proceedings of Engineering, 6(2), 3760–3767. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/10123