Application of Feature Selection for Identification of Cucumber Leaf Diseases (Cucumis sativa L.)
Abstract
According to data from BPS Kabupaten Jember, the amount of cucumber production fluctuated from 2013 to 2017. Some literature also mentions that one of the causes of the amount of cucumber production is disease attacks on these plants. Most of the cucumber plant diseases found in the leaf area such as downy mildew and powdery mildew which are both caused by fungi (fungal diseases). So far, farmers check cucumber plant diseases manually, so there is a lack of accuracy in determining cucumber plant diseases. To help farmers, a computer vision system that is able to identify cucumber diseases automatically will have an impact on the speed and accuracy of handling cucumber plant diseases. This research used 90 training data consisting of 30 healthy leaf data, 30 powdery mildew leaf data and 30 downy mildew leaf data. while for the test data as many as 30 data consisting of 10 data in each class. To get suitable parameters, a feature selection process is carried out on color features and texture features so that suitable parameters are obtained, namely: red color features, texture features consisting of contrast, Inverse Different Moment (IDM) and correlation. The K-Nearest Neighbor classification method is able to classify diseases on cucumber leaves (Cucumis sativa L.) with a training accuracy of 90% and a test accuracy of 76.67% using a variation of the value of K = 7.
Keywords
Full Text:
PDFReferences
1. KL, A., Napitupulu, M., & Jannah, N. (2015). RESPON TANAMAN MENTIMUN (Cucumis sativus L.) TERHADAP JENIS POC DAN KONSENTRASI YANG BERBEDA. AGRIFOR, XIV(1), 15–26.
BPS Kabupaten Jember. (2020). Kecamatan Arjasa Dalam Angka Tahun 2020.
Pixia, D., & Xiangdong, W. (2013). Recognition of Greenhouse Cucumber Disease Based on Image Processing Technology. Open Journal of Applied Sciences, 03(01), 27–31. https://doi.org/10.4236/ojapps.2013.31b006
Pawar, P., Turkar, V., & Patil, P. (2016). Cucumber disease detection using artificial neural network. Proceedings of the International Conference on Inventive Computation Technologies, ICICT 2016, 2016. https://doi.org/10.1109/INVENTIVE.2016.7830151
Wei, Y., Chang, R., Wang, Y., Liu, H., Du, Y., Xu, J., & Yang, L. (2012). A study of image processing on identifying cucumber disease. IFIP Advances in Information and Communication Technology, 370 AICT(PART 3), 201–209. https://doi.org/10.1007/978-3-642-27275-2_22
Jiannan, J., & Haiyan, J. (2013). Recognition for cucumber disease based on leaf spot shape and neural network. Transactions of the Chinese Society of Agricultural Engineering, 29(1).
Kaur, S., Pandey, S., & Goel, S. (2019). Plants Disease Identification and Classification Through Leaf Images: A Survey. Archives of Computational Methods in Engineering, 26(2), 507–530. https://doi.org/10.1007/s11831-018-9255-6
Khan, M. A., Akram, T., Sharif, M., Javed, K., Raza, M., & Saba, T. (2020). An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection. Multimedia Tools and Applications, 79(25–26), 18627–18656. https://doi.org/10.1007/s11042-020-08726-8
Fitri, Z. E., Purnama, I. K. E., Pramunanto, E., & Purnomo, M. H. (2017). A comparison of platelets classification from digitalization microscopic peripheral blood smear. 2017 International Seminar on Intelligent Technology and Its Application: Strengthening the Link Between University Research and Industry to Support ASEAN Energy Sector, ISITIA 2017 - Proceeding, 2017-Janua, 356–361. https://doi.org/10.1109/ISITIA.2017.8124109
Sahenda, L. N., Pumomo, M. H., Purnama, I. K. E., & Wisana, I. D. G. H. (2018). Comparison of Tuberculosis Bacteria Classification from Digital Image of Sputum Smears. 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding, 20–24. https://doi.org/10.1109/CENIM.2018.8711386
Fitri, Z. E., Syahputri, L. N. Y., & Imron, A. M. N. (2020). Classification of White Blood Cell Abnormalities for Early Detection of Myeloproliferative Neoplasms Syndrome Based on K-Nearest Neighborr. Scientific Journal of Informatics, 7(1), 136–142. https://doi.org/10.15294/sji.v7i1.24372
Fitri, Z. E., Sahenda, L. N., Puspitasari, P. S. D., Destarianto, P., Rukmi, D. L., & Imron, A. M. N. (2021). The Classification of Acute Respiratory Infection ( ARI ) Bacteria Based on K-Nearest Neighbor. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 12(2), 91–101.
Fitri, Z. E., Baskara, A., Silvia, M., Madjid, A., & Imron, A. M. N. (2021). Application of backpropagation method for quality sorting classification system on white dragon fruit ( Hylocereus undatus ). IOP Conf. Series: Earth and Environmental Science, 672(IT Agriculture), 1–6. https://doi.org/10.1088/1755-1315/672/1/012085
Fitri, Z. E., Nuhanatika, U., Madjid, A., & Imron, A. M. N. (2020). Penentuan Tingkat Kematangan Cabe Rawit (Capsicum frutescens L.) Berdasarkan Gray Level Co-Occurrence Matrix. Jurnal Teknologi Informasi Dan Terapan, 7(1), 1–5. https://doi.org/10.25047/jtit.v7i1.121
Fitri, Z. E., Rizkiyah, R., Madjid, A., & Imron, A. M. N. (2020). Penerapan Neural Network untuk Klasifkasi Kerusakan Mutu Tomat. Jurnal Rekayasa Elektrika, 16(1), 44–49. https://doi.org/10.17529/jre.v16i1.15535
DOI: https://doi.org/10.31326/jisa.v4i2.1046
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 Lalitya Nindita Sahenda, Ahmad Aris Ubaidillah, Zilvanhisna Emka Fitri, Abdul Madjid, Arizal Mujibtamala Nanda Imron
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
JOURNAL IDENTITY
Journal Name: JISA (Jurnal Informatika dan Sains)
e-ISSN: 2614-8404, p-ISSN: 2776-3234
Publisher: Program Studi Teknik Informatika Universitas Trilogi
Publication Schedule: June and December
Language: Indonesia & English
APC: The Journal Charges Fees for Publishing
Indexing: EBSCO , DOAJ, Google Scholar, Arsip Relawan Jurnal Indonesia, Directory of Research Journals Indexing, Index Copernicus International, PKP Index, Science and Technology Index (SINTA, S4) , Garuda Index
OAI address: http://trilogi.ac.id/journal/ks/index.php/JISA/oai
Contact: jisa@trilogi.ac.id
Sponsored by: DOI – Digital Object Identifier Crossref, Universitas Trilogi
In Collaboration With: Indonesian Artificial Intelligent Ecosystem(IAIE), Relawan Jurnal Indonesia, Jurnal Teknologi dan Sistem Komputer (JTSiskom)
JISA (Jurnal Informatika dan Sains) is Published by Program Studi Teknik Informatika, Universitas Trilogi under Creative Commons Attribution-ShareAlike 4.0 International License.