Comparison of Accuracy in Detecting Tomato Leaf Disease with GoogleNet VS EfficientNetB3

Authors

  • Adi Dwifana Saputra Pradita University
  • Djarot Hindarto Universitas Nasional
  • Ben Rahman Universitas Nasional
  • Handri Santoso Universitas Pradita

DOI:

10.33395/sinkron.v8i2.12218

Keywords:

Convolutional Neural Network, Dataset, EfficientNetB3, GoogleNet, Tomato leaf disease

Abstract

Tomato diseases vary greatly, one of which is tomato leaf disease. Some variants of leaf diseases include late blight, septoria leaf, yellow leaf curl virus, bacteria, mosaic virus, leaf fungus, two-spotted spider mite, and powdery mildew. By knowing the disease on tomato leaves, you can find medicine for the disease. So that it can increase the production of tomatoes with good quality and a lot of quantity. The problem that often occurs is that farmers cannot determine the disease in plants, they try to find suitable herbal medicines for their plants. After being given the drug, many plants actually died due to the pesticides given to the tomato plants. This is detrimental to tomato farmers. This problem is caused by incorrect disease detection. Therefore, this study aims to solve the problem of disease detection in tomato plants, in a more specific case, namely tomato leaves. Detection in this study uses a deep learning algorithm that uses a Convolutional Neural Network, specifically GoogleNet and EfficientNetB3. The dataset used comes from kaggle and google image. Both data sets have been pre-processed to match the data set class. Image preprocessing is performed to produce appropriate image datasets and improve performance accuracy. The dataset is trained to get the model. The training using GoogleNet resulted in an accuracy of 98.10%, loss of 0.0602 and using EfficientNetB3 resulted in an accuracy of 99.94%, loss: 0.1966. 

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References

Ahmadbeyki, A., Ghahderijani, M., Borghaee, A., & Bakhoda, H. (2023). Energy use and environmental impacts analysis of greenhouse crops production using life cycle assessment approach: A case study of cucumber and tomato from Tehran province, Iran. Energy Reports, 9, 988–999. https://doi.org/10.1016/j.egyr.2022.11.205

Alviansyah, F., Ruslianto, I., & Diponegoro, M. (2017). Identifikasi Penyakit Pada Tanaman Tomat Berdasarkan Warna Dan Bentuk Daun Dengan Metode Naive Bayes Classifier Berbasis Web. Jurnal Coding Sistem Komputer Untan, 5(1), 23–32.

Awalia, N. (2022). Identifikasi Penyakit Leaf Mold Pada Daun Tomat Menggunakan Model Densenet121 Berbasis Transfer Learning. Jurnal Ilmiah Ilmu Komputer, 8(1), 49–52. https://doi.org/10.35329/jiik.v8i1.212

de Zarzà, I., de Curtò, J., & Calafate, C. T. (2022). Detection of glaucoma using three-stage training with EfficientNet. Intelligent Systems with Applications, 16(September), 1–10. https://doi.org/10.1016/j.iswa.2022.200140

Felix, F., Faisal, S., Butarbutar, T. F. M., & Sirait, P. (2019). Implementasi CNN dan SVM untuk Identifikasi Penyakit Tomat via Daun. Jurnal SIFO Mikroskil, 20(2), 117–134. https://doi.org/10.55601/jsm.v20i2.670

Hindarto, D., & Santoso, H. (2019). Plat Nomor Kendaraan Dengan Metode Convolutional Neural Network. Jurnal Inovasi Informatika Universitas Pradita, September 2021, 1–12.

Jahandad, Sam, S. M., Kamardin, K., Amir Sjarif, N. N., & Mohamed, N. (2019). Offline signature verification using deep learning convolutional Neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3. Procedia Computer Science, 161, 475–483. https://doi.org/10.1016/j.procs.2019.11.147

Lee, A. L. S., To, C. C. K., Lee, A. L. H., Li, J. J. X., & Chan, R. C. K. (2022). Model architecture and tile size selection for convolutional neural network training for non-small cell lung cancer detection on whole slide images. Informatics in Medicine Unlocked, 28, 100850. https://doi.org/10.1016/j.imu.2022.100850

Muchtar, K., Chairuman, Yudha Nurdin, & Afdhal Afdhal. (2021). Pendeteksian Septoria pada Tanaman Tomat dengan Metode Deep Learning berbasis Raspberry Pi. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 107–113. https://doi.org/10.29207/resti.v5i1.2831

Pereira, R., Rocha, E., Pinho, D., & Santos, J. P. (2023). Convolutional neural networks for identification of moving combustion chambers entering a brazing process. Procedia Computer Science, 217(2022), 1106–1116. https://doi.org/10.1016/j.procs.2022.12.309

Purwati, E., & Khairunisa. (2007). Budidaya Tomat Dataran Rendah: Dengan Varietas Unggul Serta Tahan Hama & Penyakit. Penebar Swadaya. http://opacperpus.jogjakota.go.id/index.php/home/detail_koleksi?kd_buku=014012&id=1&kd_jns_buku=SR

Putri, A. W. (2021). Implementasi Artificial Neural Network (ANN) Backpropagation Untuk Klasifikasi Jenis Penyakit Pada Daun Tanaman Tomat. MATHunesa: Jurnal Ilmiah Matematika, 9(2), 344–350. https://doi.org/10.26740/mathunesa.v9n2.p344-350

Rozaqi, A. J., Sunyoto, A., & Arief, M. rudyanto. (2021). Deteksi Penyakit Pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network. Creative Information Technology Journal, 8(1), 22. https://doi.org/10.24076/citec.2021v8i1.263

Sangeetha, K., Ramyaa, R. B., Mousavi, A., & Radhakrishnan, M. (2023). Extraction , characterization , and application of tomato seed oil in the food industry : An updated review. Journal of Agriculture and Food Research, 11(February), 100529. https://doi.org/10.1016/j.jafr.2023.100529

Saputra, A. D., Hindarto, D., & Santoso, H. (2023). Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201. Sinkron, 8(1), 48–55. https://doi.org/10.33395/sinkron.v8i1.11906

Shabira, S. P., Hereri, A. I., & Kesumawati, E. (2020). Identifikasi Karakteristik Morfologi dan Hasil Beberapa Jenis Tanaman Tomat (Lycopersicum esculentum) di Dataran Rendah. Jurnal Ilmiah Mahasiswa Pertanian, 4(2), 51–60. https://doi.org/10.17969/jimfp.v4i2.11042

Suherman, E., Hindarto, D., Makmur, A., & Santoso, H. (2023). Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection. Sinkron, 8(1), 247–255. https://doi.org/10.33395/sinkron.v8i1.11944

Sze, E., Santoso, H., & Hindarto, D. (2022). Review Star Hotels Using Convolutional Neural Network. 7(1), 2469–2477.

Tan, M., & Le, Q. V. (2013). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Canadian Journal of Emergency Medicine, 15(3), 190. https://doi.org/10.2310/8000.2013.131108

Teerakawanich, N., Leelaruji, T., & Pichetjamroen, A. (2020). Short term prediction of sun coverage using optical flow with GoogLeNet. Energy Reports, 6, 526–531. https://doi.org/10.1016/j.egyr.2019.11.114

Uparkar, O., Bharti, J., Model, R., Pateriya, K., Ashutosh, X., Uparkar, O., Bharti, J., Pateriya, R. K., Gupta, R. K., & Sharma, A. (2023). ScienceDirect ScienceDirect Vision Transformer Outperforms Deep Convolutional Neural Network-based Model in Classifying X-ray Images Vision Transformer Outperforms Deep Convolutional Neural Network-based in Classifying. Procedia Computer Science, 218(2022), 2338–2349. https://doi.org/10.1016/j.procs.2023.01.209

Wijayani, A., & Widodo, W. (2005). Usaha Meningkatkan Kualitas Beberapa Varietas Tomat Dengan Sistem Budidaya Hidroponik. Ilmu Pertanian, 12, 77–83.

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How to Cite

Saputra, A. D., Hindarto, D. ., Rahman, B. ., & Santoso, H. . (2023). Comparison of Accuracy in Detecting Tomato Leaf Disease with GoogleNet VS EfficientNetB3. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(2), 647-656. https://doi.org/10.33395/sinkron.v8i2.12218

Issue

Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023

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Articles

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Copyright (c) 2023 Adi Dwifana Saputra Saputra

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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