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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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Disease Detection of Rice and Chili Based on Image Classification Using Convolutional Neural Network Android-Based
Keywords:
rice_and_chili_plant_diseases, image_classification, CNN, android, mobile_net.
Abstract
The current development of machine learning makes it easier for humans to obtain information, especially from images. The presence of processing assistance from machines can increase the accuracy of the information provided to further convince the recipient of the information. Rice and chili farmers in Indonesia have experienced many disease attacks from several types of plant diseases. Not many farmers understand and are good at guessing the diseases that attack their rice and chili plants. So many rice and chili farmers experienced crop failure. This research aims to build a disease-detection system for rice and chili plants based on Android-based image classification. The machine learning method used is Convolutional Neural Network (CNN) with the Mobile Net version one model combined with the Sequential CNN and Tensor Flow Lite models. The results of the transfer learning evaluation on the Mobile Net version 1 model and the sequential CNN model obtained training accuracy of 0.88% with a loss of 0.34%, validation accuracy of 0.84% with a loss of 0.40%, and testing accuracy of 86% with a loss of 43%. Each uses batch 69 of the total training data stopping at epoch 30 from epoch 100. The results of field testing on the application of rice and chili disease detection on 20 images of rice and chili plants can detect Rice Neck Blast disease with a probability of 75% to 100% and Rice Hispa with a probability of 97% to 100%. It can also detect chili plant diseases such as Chili Yellowish with a probability of 83%, Chili Leaf Spot with a probability of 99%, Chili Whitefly with a probability of 91% to 95, Chili Healthy with a probability of 78% to 99%, and Chili Leaf Curl with a probability 75 to 76%. The probability obtained varies according to how likely damage is to rice and chili plants. CNN with the Mobile Net version one model and the Sequential model can extract and classify images so that it has maximum information processing capabilities. This research can make it easier to help farmers identify diseases that attack their rice and chili plants.
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References
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Burhan, S. A., Minhas, D. S., Tariq, D. A., & Nabeel Hassan, M. (2020). Comparative Study of Deep Learning Algorithms for Disease and Pest Detection in Rice Crops. Proceedings of the 12th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2020, c. https://doi.org/10.1109/ECAI50035.2020.9223239
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Hao, X., Zhang, G., & Ma, S. (2016). Deep Learning. International Journal of Semantic Computing, 10(3), 417–439. https://doi.org/10.1142/S1793351X16500045
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Hawari, F. H., Fadillah, F., Alviandi, M. R., & Arifin, T. (2022). Klasifikasi Penyakit Tanaman Padi Menggunakan Algoritma Cnn (Convolutional Neural Network). Jurnal Responsif : Riset Sains Dan Informatika, 4(2), 184–189. https://doi.org/10.51977/jti.v4i2.856
Heviyanti, M., & Mulyani, C. (2016). Keanekaragaman Predator Serangga Hama pada Tanaman Padi Sawah (Oryza sativa L.) di Desa Paya Rahat Kecamatan Banda Mulia, Kabupaten Aceh Tamiang. Agrosamudra, 3(2), 28–37.
Hidayah, I., & Susanti, N. (2022). Peran Sektor Pertanian dalam Perekonomian Negara Maju dan Negara Berkembang : Sebuah Kajian Literatur. Jurnal Salingka Nagari, 1(1), 28–37.
Kalia, S., & Rathour, R. (2019). Current status on mapping of genes for resistance to leaf- and neck-blast disease in rice. 3 Biotech, 9(6), 1–14. https://doi.org/10.1007/s13205-019-1738-0
Kendall. (2010). System Analysis And Design (8th ed.).
Khoiruddin, M., Junaidi, A., & Saputra, W. A. (2022). Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network. Journal of Dinda : Data Science, Information Technology, and Data Analytics, 2(1), 37–45. https://doi.org/10.20895/dinda.v2i1.341
Kominfo. (2023). Gagal panen diduga jadi sebab tingginya harga beras. 10–11.
Kompas.com. (2023). Disperindag Bandung Ungkap Naiknya Harga Beras Disebabkan Gagal Panen. 1–9.
Lase, J. A., & Lestari, D. (2018). Prosiding Seminar Nasional Pertanian Peternakan Terpadu Ke-3 ISBN : 978-602-60782-2-3 Pemanfaatan Lahan Kosong Perkebunan Kelapa Untuk Hmt Utilizing Of Empty Land Coconut Farm For Fodder Forage To Increase Cattle Productivity In Maluku Utara Prosiding Sem. 167–173.
Lesmana, A. M., Fadhillah, R. P., & Rozikin, C. (2022). Identifikasi Penyakit pada Citra Daun Kentang Menggunakan Convolutional Neural Network (CNN). Jurnal Sains Dan Informatika, 8(1), 21–30. https://doi.org/10.34128/jsi.v8i1.377
Lestari, D., & Aini, L. Q. (2021). Pengujian Konsorsium Bakteri Antagonis Untuk Mengendalikan Penyakit Bercak Daun Cercospora Dan Virus Kuning Pada Tanaman Cabai Merah Besar (Capsicum annum L.) Di Kecamatan Dampit Kabupaten Malang. Jurnal Hama Dan Penyakit Tumbuhan, 9(3), 107–114. https://doi.org/10.21776/ub.jurnalhpt.2021.009.3.5
Mustakim, & Yanti, N. H. (2022). Analisis Pengaruh Kenaikan Harga Cabai Terhadap Konsumsi Usaha Mikro Kecil Dan Menengah (Umkm) Di Kuala Tungkal. Al-Mizan : Jurnal Ekonomi Syariah, 5, 39–49.
Nasional. (2022). Terserang Virus Kerdil , Ratusan Hektare Padi di Ngawi Terancam Gagal Panen. 1–8.
Nurtjahyani, S. D., & Murtini, I. (2015). Karakterisasai tanaman cabai yang terserang hama kutu kebul (Bemisia tabaci). Universiti Research Colloqium, 195–200.
Peryanto, A., Yudhana, A., & Umar, R. (2020). Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation. Journal of Applied Informatics and Computing, 4(1), 45–51. https://doi.org/10.30871/jaic.v4i1.2017
Purnamawati, A., Nugroho, W., Putri, D., & Hidayat, W. F. (2020). InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Attribution-NonCommercial 4.0 International. Some rights reserved Deteksi Penyakit Daun pada Tanaman Padi Menggunakan Algoritma Decision Tree, Random Forest, Naïve Bayes, SVM dan KNN. Jurnal Nasional Informatika Dan Teknologi Jaringan, 5(1), 212–215. https://doi.org/10.30743/infotekjar.v5i1.2934
Rahman, C. R., Arko, P. S., Ali, M. E., Iqbal Khan, M. A., Apon, S. H., Nowrin, F., & Wasif, A. (2020). Identification and recognition of rice diseases and pests using convolutional neural networks. Biosystems Engineering, 194, 112–120. https://doi.org/10.1016/j.biosystemseng.2020.03.020
Rahmi, R., Yulinda, R., & Sauqina, S. (2022). Pengaruh Konsentrasi Ekstrak Daun Nangka Belanda (Annona muricata L.) Sebagai Biopestisida Terhadap Tingkat Kematian Hama Kutu Putih (Bamisa tabaci) Pada …. Oryza: Jurnal Pendidikan …, 11(November). http://jurnal.stkipbima.ac.id/index.php/OZ/article/view/912%0Ahttps://jurnal.stkipbima.ac.id/index.php/OZ/article/download/912/515
Republika. (2023). Serangan Hama Jadi Penyebab Tingginya Harga Cabai Rawit Jatim. 9–11.
Ridho, D. (2018). Analisis Visualisasi Dan Pemetaan Data Tanaman Padi Di Indonesia Menggunakan Microsoft Power Bi. Jurnal Ilmiah Rekayasa Dan Manajemen Sistem Informasi, 4(2), 156–162.
Saputra, R. A., Wasiyanti, S., Supriyatna, A., & Saefudin, D. F. (2021). Penerapan Algoritma Convolutional Neural Network Dan Arsitektur MobileNet Pada Aplikasi Deteksi Penyakit Daun Padi. Swabumi, 9(2), 184–188. https://doi.org/10.31294/swabumi.v9i2.11678
Sulandari, S. (2006). Penyakit Daun Keriting Kuning Cabai di Indonesia. In Jurnal Perlindungan Tanaman Indonesia (Vol. 12, Issue 1, pp. 1–12).
Sunder, S., Singh, R., & Agarwal, R. (2014). Brown spot of rice: an overview History and economic importance. Indian Phytopath, 67(3), 201–215.
Surendhar, M., Anbuselvam, Y., & Ivin, J. J. S. (2021). Status of Rice Brown Spot (Helminthosporium
oryza) Management in India: A Review. Agricultural Reviews, 43(Of), 217–222. https://doi.org/10.18805/ag.r-2111
Yuliantika, G., Suprayogi, A., & Sukmono, A. (2016). Analisis Penggunaan Saluran Visibel Untuk Estimasi Kandungan Klorofil Daun Padi Dengan Citra Hymap (Studi Kasus : Kabupaten Karawang, Jawa Barat). Jurnal Geodesi Undip, 5(2), 200–207.
Yuliany, S., Aradea, & Andi Nur Rachman. (2022). Implementasi Deep Learning pada Sistem Klasifikasi Hama Tanaman Padi Menggunakan Metode Convolutional Neural Network (CNN). Jurnal Buana Informatika, 13(1), 54–65. https://doi.org/10.24002/jbi.v13i1.5022
Anggiratih, E., Siswanti, S., Octaviani, S. K., & Sari, A. (2021). Klasifikasi Penyakit Tanaman Padi Menggunakan Model Deep Learning Efficientnet B3 dengan Transfer Learning. Jurnal Ilmiah SINUS, 19(1), 75. https://doi.org/10.30646/sinus.v19i1.526
Anggraeni, D. S., Widayana, A., Rahayu, P. D., & Rozikin, C. (2022). Metode Algoritma Convolutional Neural Network pada Klasifikasi Penyakit Tanaman Cabai. STRING (Satuan Tulisan Riset Dan Inovasi Teknologi), 7(1), 73. https://doi.org/10.30998/string.v7i1.13304
Barnwal, M. K., Kotasthane, A., Magculia, N., Mukherjee, P. K., Savary, S., Sharma, A. K., Singh, H. B., Singh, U. S., Sparks, A. H., Variar, M., & Zaidi, N. (2013). A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps. European Journal of Plant Pathology, 136(3), 443–457. https://doi.org/10.1007/s10658-013-0195-6
BBC News. (2022). Di balik harga cabai yang meroket, petani kelimpungan dan kekacauan di pasar. 1–16. https://www.bbc.com/indonesia/indonesia-62046296
Bisnis, E. (2022). Serangan Penyakit Jadi Alasan Harga Cabai Melonjak. 1–8.
Burhan, S. A., Minhas, D. S., Tariq, D. A., & Nabeel Hassan, M. (2020). Comparative Study of Deep Learning Algorithms for Disease and Pest Detection in Rice Crops. Proceedings of the 12th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2020, c. https://doi.org/10.1109/ECAI50035.2020.9223239
Christian, I., Dewi, L. P., & Andjarwirawan, J. (2017). Sistem Informasi Manajemen pada Apotek Christ Berbasis Website. Jurnal Infra, 5(1), 2–6. publlication.petra.ac.id
Desi Ernawati, Riki Andri Yusda, G. M. P. (2021). Analisis Penyakit Pada Tanaman Cabai Dengan Metode Case Based Reasoning Berbasis Web. International Journal of Intellectual Discourse (IJID), 39(8), 102–111.
Hao, X., Zhang, G., & Ma, S. (2016). Deep Learning. International Journal of Semantic Computing, 10(3), 417–439. https://doi.org/10.1142/S1793351X16500045
Hasanah, L. (2022). Analisis Faktor-Faktor Pengaruh Terjadinya Impor Beras di Indonesia Setelah Swasembada Pangan. Growth: Jurnal Ilmiah Ekonomi Pembangunan, 1(2), 57–72.
Hawari, F. H., Fadillah, F., Alviandi, M. R., & Arifin, T. (2022). Klasifikasi Penyakit Tanaman Padi Menggunakan Algoritma Cnn (Convolutional Neural Network). Jurnal Responsif : Riset Sains Dan Informatika, 4(2), 184–189. https://doi.org/10.51977/jti.v4i2.856
Heviyanti, M., & Mulyani, C. (2016). Keanekaragaman Predator Serangga Hama pada Tanaman Padi Sawah (Oryza sativa L.) di Desa Paya Rahat Kecamatan Banda Mulia, Kabupaten Aceh Tamiang. Agrosamudra, 3(2), 28–37.
Hidayah, I., & Susanti, N. (2022). Peran Sektor Pertanian dalam Perekonomian Negara Maju dan Negara Berkembang : Sebuah Kajian Literatur. Jurnal Salingka Nagari, 1(1), 28–37.
Kalia, S., & Rathour, R. (2019). Current status on mapping of genes for resistance to leaf- and neck-blast disease in rice. 3 Biotech, 9(6), 1–14. https://doi.org/10.1007/s13205-019-1738-0
Kendall. (2010). System Analysis And Design (8th ed.).
Khoiruddin, M., Junaidi, A., & Saputra, W. A. (2022). Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network. Journal of Dinda : Data Science, Information Technology, and Data Analytics, 2(1), 37–45. https://doi.org/10.20895/dinda.v2i1.341
Kominfo. (2023). Gagal panen diduga jadi sebab tingginya harga beras. 10–11.
Kompas.com. (2023). Disperindag Bandung Ungkap Naiknya Harga Beras Disebabkan Gagal Panen. 1–9.
Lase, J. A., & Lestari, D. (2018). Prosiding Seminar Nasional Pertanian Peternakan Terpadu Ke-3 ISBN : 978-602-60782-2-3 Pemanfaatan Lahan Kosong Perkebunan Kelapa Untuk Hmt Utilizing Of Empty Land Coconut Farm For Fodder Forage To Increase Cattle Productivity In Maluku Utara Prosiding Sem. 167–173.
Lesmana, A. M., Fadhillah, R. P., & Rozikin, C. (2022). Identifikasi Penyakit pada Citra Daun Kentang Menggunakan Convolutional Neural Network (CNN). Jurnal Sains Dan Informatika, 8(1), 21–30. https://doi.org/10.34128/jsi.v8i1.377
Lestari, D., & Aini, L. Q. (2021). Pengujian Konsorsium Bakteri Antagonis Untuk Mengendalikan Penyakit Bercak Daun Cercospora Dan Virus Kuning Pada Tanaman Cabai Merah Besar (Capsicum annum L.) Di Kecamatan Dampit Kabupaten Malang. Jurnal Hama Dan Penyakit Tumbuhan, 9(3), 107–114. https://doi.org/10.21776/ub.jurnalhpt.2021.009.3.5
Mustakim, & Yanti, N. H. (2022). Analisis Pengaruh Kenaikan Harga Cabai Terhadap Konsumsi Usaha Mikro Kecil Dan Menengah (Umkm) Di Kuala Tungkal. Al-Mizan : Jurnal Ekonomi Syariah, 5, 39–49.
Nasional. (2022). Terserang Virus Kerdil , Ratusan Hektare Padi di Ngawi Terancam Gagal Panen. 1–8.
Nurtjahyani, S. D., & Murtini, I. (2015). Karakterisasai tanaman cabai yang terserang hama kutu kebul (Bemisia tabaci). Universiti Research Colloqium, 195–200.
Peryanto, A., Yudhana, A., & Umar, R. (2020). Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation. Journal of Applied Informatics and Computing, 4(1), 45–51. https://doi.org/10.30871/jaic.v4i1.2017
Purnamawati, A., Nugroho, W., Putri, D., & Hidayat, W. F. (2020). InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Attribution-NonCommercial 4.0 International. Some rights reserved Deteksi Penyakit Daun pada Tanaman Padi Menggunakan Algoritma Decision Tree, Random Forest, Naïve Bayes, SVM dan KNN. Jurnal Nasional Informatika Dan Teknologi Jaringan, 5(1), 212–215. https://doi.org/10.30743/infotekjar.v5i1.2934
Rahman, C. R., Arko, P. S., Ali, M. E., Iqbal Khan, M. A., Apon, S. H., Nowrin, F., & Wasif, A. (2020). Identification and recognition of rice diseases and pests using convolutional neural networks. Biosystems Engineering, 194, 112–120. https://doi.org/10.1016/j.biosystemseng.2020.03.020
Rahmi, R., Yulinda, R., & Sauqina, S. (2022). Pengaruh Konsentrasi Ekstrak Daun Nangka Belanda (Annona muricata L.) Sebagai Biopestisida Terhadap Tingkat Kematian Hama Kutu Putih (Bamisa tabaci) Pada …. Oryza: Jurnal Pendidikan …, 11(November). http://jurnal.stkipbima.ac.id/index.php/OZ/article/view/912%0Ahttps://jurnal.stkipbima.ac.id/index.php/OZ/article/download/912/515
Republika. (2023). Serangan Hama Jadi Penyebab Tingginya Harga Cabai Rawit Jatim. 9–11.
Ridho, D. (2018). Analisis Visualisasi Dan Pemetaan Data Tanaman Padi Di Indonesia Menggunakan Microsoft Power Bi. Jurnal Ilmiah Rekayasa Dan Manajemen Sistem Informasi, 4(2), 156–162.
Saputra, R. A., Wasiyanti, S., Supriyatna, A., & Saefudin, D. F. (2021). Penerapan Algoritma Convolutional Neural Network Dan Arsitektur MobileNet Pada Aplikasi Deteksi Penyakit Daun Padi. Swabumi, 9(2), 184–188. https://doi.org/10.31294/swabumi.v9i2.11678
Sulandari, S. (2006). Penyakit Daun Keriting Kuning Cabai di Indonesia. In Jurnal Perlindungan Tanaman Indonesia (Vol. 12, Issue 1, pp. 1–12).
Sunder, S., Singh, R., & Agarwal, R. (2014). Brown spot of rice: an overview History and economic importance. Indian Phytopath, 67(3), 201–215.
Surendhar, M., Anbuselvam, Y., & Ivin, J. J. S. (2021). Status of Rice Brown Spot (Helminthosporium
oryza) Management in India: A Review. Agricultural Reviews, 43(Of), 217–222. https://doi.org/10.18805/ag.r-2111
Yuliantika, G., Suprayogi, A., & Sukmono, A. (2016). Analisis Penggunaan Saluran Visibel Untuk Estimasi Kandungan Klorofil Daun Padi Dengan Citra Hymap (Studi Kasus : Kabupaten Karawang, Jawa Barat). Jurnal Geodesi Undip, 5(2), 200–207.
Yuliany, S., Aradea, & Andi Nur Rachman. (2022). Implementasi Deep Learning pada Sistem Klasifikasi Hama Tanaman Padi Menggunakan Metode Convolutional Neural Network (CNN). Jurnal Buana Informatika, 13(1), 54–65. https://doi.org/10.24002/jbi.v13i1.5022
Published
2023-09-01
How to Cite
Muslim, R., Zaeniah, Z., Akbar, A., Imran, B., & Zaenudin, Z. (2023). Disease Detection of Rice and Chili Based on Image Classification Using Convolutional Neural Network Android-Based. Jurnal Pilar Nusa Mandiri, 19(2), 85-96. https://doi.org/10.33480/pilar.v19i2.4669
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Copyright (c) 2023 Rudi Muslim, Zaeniah Zaeniah, Ardiyallah Akbar, Bahtiar Imran, Zaenudin Zaenudin
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