DOI : https://doi.org/10.62527/ijasce.6.2.203

Disease Identification on Fig Leaf Images Using Deep Learning Method

Waeisul Bismi (1), Dwiza Riana (2), Alya Shafira Hewiz (3)
(1) Faculty of Information Technology, Graduate School of Computer Science, Nusa Mandiri University, Makasar, Jakarta, Indonesia
(2) Faculty of Information Technology, Graduate School of Computer Science, Nusa Mandiri University, Makasar, Jakarta, Indonesia
(3) Faculty of Medicine, Airlangga University, Tambaksari, Surabaya, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Bismi, W., Riana, D., & Hewiz , A. S. (2024). Disease Identification on Fig Leaf Images Using Deep Learning Method. International Journal of Advanced Science Computing and Engineering, 6(2), 57–63. https://doi.org/10.62527/ijasce.6.2.203
Citation Format :

The fig plant, known as Ficus carica, has been cultivated worldwide, including in Indonesia. It has nutritional benefits and medicinal properties. However, there are still difficulties in growing it, making the plant scarce. The scarcity of fig plants in Indonesia is mainly due to the threat of diseases and viruses that affect them. There are various diseases that affect fig plants, including leaf rust (Cerotelium fici), mosaic disease, and Bemisia tabaci (whitefly) disease. Infected fig plants become unhealthy, experiencing stunted growth and deformed fruits thus it is necessary to identify the diseases accurately using technological assistance. This research aimed to automatically identify diseases in fig leaves. The method started by digitizing fig leaf images and confirming with botanical experts specializing in fig plants to label the types of diseases present. The research produced a dataset of fig leaf images consisting of four classes of fig leaves: Cerotelium fici, mosaic disease, whitefly, and healthy fig leaves. The dataset resulted in confirmed 300 fig leaf images. The augmentation techniques were applied to increase images to 3,300 fig leaf images. This dataset was then divided into subsets for training, validation, and testing. For the classification and identification, a Deep Learning approach was used with three models: VGG16, VGG19, and MobileNet. Among these models, MobileNet achieved the highest accuracy of 98.79%. Subsequently, the identification system was implemented by converting the generated model into TensorFlow Lite and integrating it into the Android Studio software, enabling it to function as a mobile application on Android devices.

I. F. ul Rasool et al., “Industrial Application and Health Prospective of Fig (Ficus carica) By-Products,” Molecules, vol. 28, no. 3, p. 960, Jan. 2023, doi: 10.3390/molecules28030960.

M. I. Barolo, N. Ruiz Mostacero, and S. N. López, “Ficus carica L. (Moraceae): An ancient source of food and health,” Food Chemistry, vol. 164, pp. 119–127, Dec. 2014, doi: 10.1016/j.foodchem.2014.04.112.

B. Y. Sheikh, “The role of prophetic medicine in the management of diabetes mellitus: A review of literature,” Journal of Taibah University Medical Sciences, vol. 11, no. 4, pp. 339–352, Aug. 2016, doi: 10.1016/j.jtumed.2015.12.002.

S. van Noort, R. Wang, and S. G. Compton, “Fig Wasps (Hymenoptera: Chalcidoidea: Agaonidae, Pteromalidae) Associated with Asian Fig Trees (Ficus, Moraceae) in Southern Africa: Asian Followers and African Colonists,” African Invertebrates, vol. 54, no. 2, pp. 381–400, Dec. 2013, doi: 10.5733/afin.054.0208.

M. Trad, C. Le Bourvellec, B. Gaaliche, C. M. G. C. Renard, and M. Mars, “Nutritional Compounds in Figs from the Southern Mediterranean Region,” International Journal of Food Properties, vol. 17, no. 3, pp. 491–499, Nov. 2013, doi: 10.1080/10942912.2011.642447.

B. T.K, M. S.K, and S. D., Fruits : Tropical and Subtropical. Volume 1, 3rd ed. India: Naya Udyog, 2021. [Online]. Available: https://www.cabdirect.org/cabdirect/abstract/20013102347

O. Boyacioglu, B. Kara, H. Can, T. N. Yerci, S. Yilmaz, and S. O. Boyacioglu, “Leaf Hexane Extracts of Two Turkish Fig (Ficus carica L.) Cultivars Show Cytotoxic Effects on a Human Prostate Cancer Cell Line,” Agriculture and Food Sciences Research, vol. 6, no. 1, pp. 66–70, 2019, doi: 10.20448/journal.512.2019.61.66.70.

M. Qomaruddin, D. Riana, and A. Anton, “Segmentasi K-Means Citra Daun Tin Dengan Klasifikasi Ciri Gray Level Co Occurance Matrix,” Jurnal Sistem dan Teknologi Informasi (Justin), vol. 9, no. 2, p. 223, Apr. 2021, doi: 10.26418/justin.v9i2.44139.

Devi Revita Amlia and Siti Hazar, “Karakterisasi Simplisia Daun Tin (Ficus Carica L.),” Jurnal Riset Farmasi, pp. 119–124, Dec. 2022, doi: 10.29313/jrf.v2i2.1447

H. A. Begum, “Antimicrobial, antioxidant, phytochemical and pharmacognostic study of the leaf powder of Ficus carica L.,” Pure and Applied Biology, vol. 9, no. 1, Mar. 2020, doi: 10.19045/bspab.2020.90105.

L. Rezagholizadeh, M. Aghamohammadian, M. Oloumi, S. Banaei, M. Mazani, and M. Ojarudi, “Inhibitory effects of Ficus carica and Olea europaea on pro-inflammatory cytokines: A review,” Iran. J. Basic Med. Sci., vol. 25, no. 3, pp. 268–275, 2022, doi: 10.22038/ijbms.2022.60954.13494.

S. Ben-Shabat, L. Yarmolinsky, D. Porat, and A. Dahan, “Antiviral effect of phytochemicals from medicinal plants: Applications and drug delivery strategies,” Drug Delivery and Translational Research, vol. 10, no. 2, pp. 354–367, Dec. 2019, doi: 10.1007/s13346-019-00691-6.

K. Zidi et al., “The use of modified atmosphere packaging as mean of bioactive compounds and antioxidant activities preservation of fresh figs (Ficus carica L.) from rare cultivars,” The Annals of the University Dunarea de Jos of Galati Fascicle VI – Food Technology, vol. 44, no. 1, pp. 149–164, Jun. 2020, doi: 10.35219/foodtechnology.2020.1.09.

W. Fajar and T. Mulyani, “Review Artikel: Etnofarmakologi Tanaman Tin (Ficus Carica L.) (Kajian Tafsir Ilmi Tentang Buah Tin Dalam Al-Qur’an),” Jurnal Farmagazine, vol. 7, no. 1, p. 58, Feb. 2020, doi: 10.47653/farm.v7i1.156.

“Green Synthesis of Iron Oxide Nanoparticles by Using Ficus Carica Leaf Extract and Its Antioxidant Activity,” Biointerface Research in Applied Chemistry, vol. 12, no. 2, pp. 2108–2116, Jun. 2021, doi: 10.33263/briac122.21082116.

I. P. B. Arthana, “Kajian potensi antidiabetik ekstrak daun tin ( Ficus carica L.) dengan metode in vivo,” Fak. Ilmu Kesehat. Univ. Ngudi Waluyo, 2020.

S.-H. Li, P. H. Zheng, I. C. Chiang, Y. T. Su, S. S. Lin, and C.-Z. Ho, “The Development and Evaluation of Fig Leaf Syrup,” Proceedings of International Conference on Artificial Life and Robotics, vol. 25, pp. 357–359, Jan. 2020, doi: 10.5954/icarob.2020.pos7-1.

A. Zakaria, Z. Yahya, and H. Nurmayunita, “Pengaruh Pemberian Teh Dauh Tin Terhadap Kadar Gula Darah pada Penderita Diabetes Mellitus,” Jurnal Ilmu Kesehatan, vol. 7, no. 2, p. 357, May 2019, doi: 10.32831/jik.v7i2.215.

Kamas, M. Nesbitt, and L. Stein, “Texas Fruit and Nut Production,” Texas A&M AgriLife Ext., pp. 1–7, 2017.

S. Das, “Classification of Fig Leaf Diseases using Deep Convolutional Neural Network and Transfer Learning,” in 7th International Conference on Signal Processing and Integrated Networks (SPIN), 2020, pp. 427–432.

A. Srivastas and P. Singh, “Identification and classification of Ficus carica leaves using image processing techniques,” in International Conference on Computing and Communication Systems, 2021, pp. 613–619.

E. A. Rogovski Czaja, W. M. Zeviani, M. Dalla Pria, and L. L. May De Mio, “Monocycle components of fig rust comparing in vivo and ex vivo methodology,” European Journal of Plant Pathology, vol. 160, no. 4, pp. 813–823, May 2021, doi: 10.1007/s10658-021-02284-x.

Z. Alsaheli et al., “Development of singleplex and multiplex real-time (Taqman®) RT-PCR assays for the detection of viruses associated with fig mosaic disease,” Journal of Virological Methods, vol. 293, p. 114145, Jul. 2021, doi: 10.1016/j.jviromet.2021.114145.

S. Hadianti and D. Riana, “Segmentation and analysis of Pap smear microscopic images using the K-means and J48 algorithms,” Jurnal Teknologi dan Sistem Komputer, vol. 9, no. 2, pp. 113–119, Mar. 2021, doi: 10.14710/jtsiskom.2021.13943.

D. Riana, S. Rahayu, S. Hadianti, Frieyadie, M. Hasan, and R. Pratama, “Identifikasi Citra Pap Smear RepoMedUNM dengan Menggunakan K-Means Clustering dan GLCM,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, pp. 1–2, 2022.

K. H. Mahmud, Adiwijaya, and S. Al Faraby, “Klasifikasi Citra Multi-Kelas Menggunakan Convolutional Neural Network,” e-Proceeding Eng., vol. 6, no. 1, pp. 2127–2136, 2019.

I. Kurniastuti, E. N. I. Yuliati, F. Yudianto, and T. D. Wulan, “Determination of Hue Saturation Value (HSV) color feature in kidney histology image,” Journal of Physics: Conference Series, vol. 2157, no. 1, p. 012020, Jan. 2022, doi: 10.1088/1742-6596/2157/1/012020.

R. Pujiati and N. Rochmawati, “Identifikasi Citra Daun Tanaman Herbal Menggunakan Metode Convolutional Neural Network ( CNN ),” JINACS (Journal Informatics Comput. Sci., vol. 03, pp. 351–357, 2022.

T. Annas T.S, “Perbandingan Model Warna RGB , HSL dan HSV Sebagai Fitur dalam Prediksi Cuaca pada Citra Langit menggunakan,” Tek. Inform., p. 9, 2019.

J. Lu, L. Tan, and H. Jiang, “Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification,” Agriculture, vol. 11, no. 8, p. 707, Jul. 2021, doi: 10.3390/agriculture11080707.

M. D. Muafa, “Pengembangan Aplikasi Berbasis Web dengan Rshiny untuk Data Klasifikasi Menggunakan Metode Naive Bayes,” Automata, vol. 3, no. 1, p. 8, 2022, [Online]. Available: https://journal.uii.ac.id/AUTOMATA/article/view/21875

R. Agustina, R. Magdalena, and N. O. R. K. Caecar, “Klasifikasi Kanker Kulit menggunakan Metode Convolutional Neural Network dengan Arsitektur VGG-16,” ELKOMIKA, vol. 10, no. 2, pp. 446–457, 2022, [Online]. Available: https://ejurnal.itenas.ac.id/index.php/elkomika/article/view/5674/2879

J. Pardede, B. Sitohang, S. Akbar, and M. L. Khodra, “Implementation of Transfer Learning Using VGG16 on Fruit Ripeness Detection,” International Journal of Intelligent Systems and Applications, vol. 13, no. 2, pp. 52–61, Apr. 2021, doi: 10.5815/ijisa.2021.02.04.

R. Rismiyati and A. Luthfiarta, “VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification,” Telematika, vol. 18, no. 1, p. 37, Mar. 2021, doi: 10.31315/telematika.v18i1.4025.

B. Han, J. Du, Y. Jia, and H. Zhu, “Zero-Watermarking Algorithm for Medical Image Based on VGG19 Deep Convolution Neural Network,” Journal of Healthcare Engineering, vol. 2021, pp. 1–12, Jul. 2021, doi: 10.1155/2021/5551520.

J. Feriawan, D. Swanjaya, T. Informatika, F. Teknik, U. Nusantara, and P. Kediri, “Perbandingan Arsitektur Visual Geometry Group dan MobileNet Pada Pengenalan Jenis Kayu,” Semin. Nas. Inov. Teknol. UN PGRI, pp. 185–190, 2020.

P. N. Zakiya, L. Novamizanti, S. Rizal, and U. Telkom, “Klasifikasi Patologi Makula Retina Melalui Citra OCT Menggunakan Convolutional Neutral Network dengan Arsitektur Mobilenet,” e-Proceding of Enggineering, vol. 8, no. 5, pp. 5072–5082, 2021.

D. Kurniawan and A. Saputra, “Penerapan K-Nearest Neighbour Dalam Penerimaan Peserta Didik Dengan Sistem Zonasi,” J. Sist. Inf. Bisnis, vol. 02, no. 51, pp. 212–219, 2019.

Labrèche, D. Evans, D. Marszk, and ..., “OPSSAT Spacecraft Autonomy with TensorFlow Lite, Unsupervised Learning, and Online Machine Learning,” 2022 IEEE Aerosp., no. March, 2022, [Online]. Available: https://www.researchgate.net/publication/358040622_OPS-SAT_Spacecraft_Autonomy_with_TensorFlow_Lite_Unsupervised_Learning_and_Online_Machine_Learning

“TensorFlow Lite Guide,” TensorFlow, 2022. https://www.tensorflow.org/lite/guide

R. David, J. Duke, A. Jain, V. J. Reddi, N. Jeffries, and J. Li, “Tensorflow Lite Micro: Embedded Machine Learning On Tinyml Systems,” MLSys Proc., pp. 1–9, 2021.

W. Bismi, M. Napiah, J. L. Putra, and F. Shidiq, “Rancang Bangun Aplikasi Pembelajaran Bahasa Arab Untuk Siswa Madrasah Ibtidaiyah Berbasis Android,” J. CO-SCIENCE, vol. 1, p. 8, 2021.

W. Bismi, M. Maysaroh, and T. Asra, “Rancang Bangun Aplikasi Pembelajaran Mahfudzot Untuk Pondok Pesantren Berbasis Android Menggunakan Metode Extreme Programming,” Semnas Ristek (Seminar Nas. Ris. dan Inov. Teknol., vol. 4, no. 1, pp. 15–21, 2020.