Application of the KNN method to check soil compatibility using a microcontroller for android-based banyuwangi citrus fruit plants

Main Article Content

Solehatin Solehatin
Hadiq Hadiq
Dwika Ananda Agustina Pertiwi

Abstract

The city of Banyuwangi needs a touch of information technology in the agricultural sector, namely in the process of planting orange fruit, because orange fruit planting is carried out continuously to meet export needs. Citrus fruit planting is sometimes carried out without paying attention to the existing soil nutrient content, this condition can result in less than optimal harvest results. The research was carried out by creating a soil nutrient detection application with the aim of providing information to farmers about the soil nutrient content including nitrogen, calcium, phosphorus, pH and moisture resistance before planting citrus fruit. From the results of trials conducted by researchers with farmers based on various types of soil used as trial data, the information shows a match of 89.6%. The results of the research produced an Android-based soil nutrient checking application that farmers can use to check soil nutrients when planting citrus fruit. In conducting the research, the researcher created an application by applying the KNN method and utilizing a microcontroller to input the data. By combining methods and tools, microcontrollers can assist the implementation process so as to provide information in the form of soil suitability for planting citrus fruit based on the nutrient content of the soil being examined. The contribution made from the research results is the application of a KNN method which is used to check soil nutrients so that it can maximize the results of the detection carried out. Meanwhile, another contribution is the use of a tool in the form of a microcontroller which is used to automatically input data which can be obtained using the Bluetooth service in the soil nutrient check application.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
S. Solehatin, H. Hadiq, and D. A. A. Pertiwi, “Application of the KNN method to check soil compatibility using a microcontroller for android-based banyuwangi citrus fruit plants”, J. Soft Comput. Explor., vol. 4, no. 3, pp. 123 - 132, Sep. 2023.
Section
Articles

References

E. Amalia, “Analisis Rantai Pasok (Supply Chain) Kopi Robusta Di Dusun Gondang, Desa Darungan, Kecamtan Tanggul, Kabupaten Jember.” Institut Agama Islam Negeri Jember, 2020.

U. N. Mufida Husna, “Pemetaan Tingkat Produktivitas Jeruk Siam (Citrus nobilis) dengan Sistem Pengolahan Mandiri di Kabupaten Banyuwangi.” Politeknik Negeri jember, 2023.

J. C. Kilmanun, T. Purbiaty, and T. Sugiarti, “Pengembangan Kawasan Jeruk Berbasis Korporasi di Kabupaten Banyuwangi Jawa Timur The Development of Citrus Area With Coorporation Based in Banyuwangi East Java,” in Prosiding Seminar Nasional, 2020, vol. 96.

K. D. Susila, “Studi Keharaan Tanaman dan Evaluasi Kesuburan Tanah di Lahan Pertanaman Jeruk Desa Cenggiling , Kecamatan Kuta Selatan,” vol. 3, no. 2, pp. 13–20, 2013.

A. A. Wiguna and L. E. Widyatami, “Penerapan Sistem Tabulampot Pada Jenis Tanaman Mangga dan Jeruk di Kelurahan Karangrejo Kecamatan Sumbersari Kabupaten Jember,” Semin. Has. Penelit. dan Pengabdi. Masy. Dana BOPTN Tahun 2016, ISBN 978-602-14917-3-7 Penerapan, pp. 211–214, 2016.

A. Chusyairi et al., “Aplikasi E-Soil untuk Mengidentifikasi Warna Tanah Berbasis Android Menggunakan Munsell Soil Color Chart E-SOIL APPLICATION TO IDENTIFY SOIL COLORS BASED ON ANDROID USING THE MUNSELL SOIL COLOR CHART,” Maret 2019 IJCCS, vol. 09, no. 01, pp. 1–5, 2019.

R. S. ARSLAN and A. H. Yurttakal, “K-Nearest Neighbour Classifier Usage for Permission Based Malware Detection in Android,” Icontech Int. J., vol. 4, no. 2, pp. 15–27, 2020, doi: 10.46291/icontechvol4iss2pp15-27.

H. Pratama, A. Yunan, and R. Arif Candra, “Design and Build a Soil Nutrient Measurement Tool for Citrus Plants Using NPK Soil Sensors Based on the Internet of Things,” Brill. Res. Artif. Intell., vol. 1, no. 2, pp. 67–74, 2021, doi: 10.47709/brilliance.v1i2.1300.

R. Widowati, “Keberhasilan Okulasi Varietas Jeruk Manis Pada Berbagai Dosis Pupuk Majemuk Npk,” AgroSainT, vol. 8, no. 1, pp. 56–61, 2017.

S. Widyastuti, “Penerapan Konsep Kurikulum Hijau dan Kimia Hijau dalam Desain Praktikum dan Pengolahan Limbah Laboratorium Kimia,” Indones. Green Technol. J., vol. 11, no. 01, pp. 38–45, 2022, doi: 10.21776/ub.igtj.2022.011.01.03.

V. Manivasan, “Soil & Water Compatibility Testing Based on IOT,” pp. 332–334.

F. S. Harahap, H. Walida, and I. Arman, Dasar-dasar Agronomi Pertanian. CV. Mitra Cendekia Media, 2021.

N. Senesi and E. Loffredo, “The chemistry of soil organic matter,” in Soil physical chemistry, CRC press, 2018, pp. 239–370.

A. Kicińska, R. Pomykała, and M. Izquierdo‐Diaz, “Changes in soil pH and mobility of heavy metals in contaminated soils,” Eur. J. Soil Sci., vol. 73, no. 1, p. e13203, 2022.

W. J. Werner, J. Sanderman, and J. M. Melillo, “Decreased soil organic matter in a long‐term soil warming experiment lowers soil water holding capacity and affects soil thermal and hydrological buffering,” J. Geophys. Res. Biogeosciences, vol. 125, no. 4, p. e2019JG005158, 2020.

H. Dvořáčková, J. Dvořáček, P. Hueso González, and V. Vlček, “Effect of different soil amendments on soil buffering capacity,” PLoS One, vol. 17, no. 2, p. e0263456, 2022.

A. Colbert, N. Yee, and G. George, “The digital workforce and the workplace of the future,” Academy of management journal, vol. 59, no. 3. Academy of Management Briarcliff Manor, NY, pp. 731–739, 2016.

D. Reed, D. Gannon, and J. Dongarra, “Reinventing high performance computing: challenges and opportunities,” arXiv Prepr. arXiv2203.02544, 2022.

K. Golicz, S. Hallett, R. Sakrabani, and J. Ghosh, “Adapting smartphone app used in water testing, for soil nutrient analysis,” Comput. Electron. Agric., vol. 175, no. October 2019, p. 105532, 2020, doi: 10.1016/j.compag.2020.105532.

E. C. Hobbs, A. Colling, R. B. Gurung, and J. Allen, “The potential of diagnostic point‐of‐care tests (POCTs) for infectious and zoonotic animal diseases in developing countries: Technical, regulatory and sociocultural considerations,” Transbound. Emerg. Dis., vol. 68, no. 4, pp. 1835–1849, 2021.

G. B. Raja, “Impact of internet of things, artificial intelligence, and blockchain technology in Industry 4.0,” Internet Things, Artif. Intell. Blockchain Technol., pp. 157–178, 2021.

S. Kumar, P. Tiwari, and M. Zymbler, “Internet of Things is a revolutionary approach for future technology enhancement: a review,” J. Big data, vol. 6, no. 1, pp. 1–21, 2019.

E. Z. Ferdousy, M. M. Islam, and M. A. Matin, “Combination of Naïve Bayes Classifier and K-Nearest Neighbor (cNK) in the Classification Based Predictive Models,” Comput. Inf. Sci., vol. 6, no. 3, 2013, doi: 10.5539/cis.v6n3p48.

R. Pambudi and F. Madani, “Analysis of public opinion sentiment against COVID-19 in Indonesia on twitter using the k-nearest neighbor algorithm and decision tree,” J. Soft Comput. Explor., vol. 3, no. 2, pp. 117–122, 2022, doi: 10.52465/joscex.v3i2.88.

K. Taunk, S. De, S. Verma, and A. Swetapadma, “A brief review of nearest neighbor algorithm for learning and classification,” in 2019 international conference on intelligent computing and control systems (ICCS), 2019, pp. 1255–1260.

O. Adepoju, J. Wosowei, and H. Jaiman, “Comparative evaluation of credit card fraud detection using machine learning techniques,” in 2019 Global Conference for Advancement in Technology (GCAT), 2019, pp. 1–6.

A. Nurdina and A. B. I. Puspita, “Naive Bayes and KNN for Airline Passenger Satisfaction Classification: Comparative Analysis,” J. Inf. Syst. Explor. Res., vol. 1, no. 2, 2023.

S. Hota and S. Pathak, “KNN classifier based approach for multi-class sentiment analysis of twitter data,” Int. J. Eng. Technol., vol. 7, no. 3, pp. 1372–1375, 2018, doi: 10.14419/ijet.v7i3.12656.

S. Nayak, M. Bhat, N. V. S. Reddy, and B. A. Rao, “Study of distance metrics on k - Nearest neighbor algorithm for star categorization,” J. Phys. Conf. Ser., vol. 2161, no. 1, 2022, doi: 10.1088/1742-6596/2161/1/012004.

Y. Yao, L. Zheng, X. Yang, M. Naphade, and T. Gedeon, “Simulating content consistent vehicle datasets with attribute descent,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16, 2020, pp. 775–791.

M. A. Imron and B. Prasetiyo, “Improving Algorithm Accuracy K-Nearest Neighbor Using Z-Score Normalization and Particle Swarm Optimization to Predict Customer Churn,” J. Soft Comput. Explor., vol. 1, no. 1, pp. 56–62, 2020, doi: 10.52465/joscex.v1i1.7.

A. Fitriandi, E. Komalasari, H. G.-J. R. dan, and undefined 2016, “Rancang Bangun Alat Monitoring Arus dan Tegangan Berbasis Mikrokontroler dengan SMS Gateway,” Academia.Edu, vol. 10, no. 2, 2016.

L. Burton, K. Jayachandran, and S. Bhansali, “Review—The ‘Real-Time’ Revolution for In situ Soil Nutrient Sensing,” J. Electrochem. Soc., vol. 167, no. 3, p. 037569, 2020, doi: 10.1149/1945-7111/ab6f5d.

A. Lisowska, B. Filipek-Mazur, A. Kalisz, O. Gorczyca, and A. Kowalczyk, “Changes in Soil Sulfate Sulfur Content as an Effect of Fertilizer Granules Containing Elemental Sulfur, Halloysite and Phosphate Rock,” Agronomy, vol. 13, no. 5, p. 1410, 2023.

L. Grimond, D. Rivest, S. Bilodeau-Gauthier, R. Khlifa, R. Elferjani, and N. Bélanger, “Novel soil reconstruction leads to successful afforestation of a former asbestos mine in southern Quebec, Canada,” New For., pp. 1–27, 2023.

A. D. B. Sadewo, E. R. Widasari, and A. Muttaqin, “Perancangan Pengendali Rumah menggunakan Smartphone Android dengan Konektivitas Bluetooth,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 5, pp. 415–425, 2017.

M. Santo Gitakarma and T. K. Priyambodo, “A real-time smart home system using android Bluetooth control device module,” in 2019 International Symposium on Electronics and Smart Devices (ISESD), 2019, pp. 1–7.

U. A. Ramadhani, I. D. G. H. Wisana, and P. C. Nugraha, “Smartphone based respiratory signal monitoring and apnea detection via bluetooth comunication,” J. Teknokes, vol. 14, no. 2, pp. 49–55, 2021.

J. Andi, “Pembangunan Aplikasi Child Tracker Berbasis Assisted – Global Positioning System ( A-GPS ) Dengan Platform Android,” J. Ilm. Komput. dan Inform., vol. 1, no. 1, pp. 1–8, 2015.

S. Postolache, P. Sebastião, V. Viegas, O. Postolache, and F. Cercas, “IoT-Based Systems for Soil Nutrients Assessment in Horticulture,” Sensors, vol. 23, no. 1, 2023, doi: 10.3390/s23010403.

T. Toyao, Z. Maeno, S. Takakusagi, T. Kamachi, I. Takigawa, and K. Shimizu, “Machine learning for catalysis informatics: recent applications and prospects,” Acs Catal., vol. 10, no. 3, pp. 2260–2297, 2019.

H. S. El Imanni et al., “Multispectral UAV data for detection of weeds in a citrus farm using machine learning and Google Earth Engine: Case study of Morocco,” Remote Sens. Appl. Soc. Environ., vol. 30, p. 100941, 2023.

C. Dewi and Y. K. Arbawa, “Performance evaluation of distance function in KNN and WKNN for classification of soil organic matter,” in 2019 International Conference on Sustainable Information Engineering and Technology (SIET), 2019, pp. 196–199.

A. Erler, D. Riebe, T. Beitz, H. G. Löhmannsröben, and R. Gebbers, “Soil nutrient detection for precision agriculture using handheld laser-induced breakdown spectroscopy (LIBS) and multivariate regression methods (PLSR, lasso and GPR),” Sensors (Switzerland), vol. 20, no. 2, 2020, doi: 10.3390/s20020418.

R. Madhumathi, T. Arumuganathan, and R. Shruthi, “Soil Nutrient Detection and Recommendation Using IoT and Fuzzy Logic,” Comput. Syst. Sci. Eng., vol. 43, no. 2, pp. 455–469, 2022, doi: 10.32604/csse.2022.023792.

Abstract viewed = 189 times

Most read articles by the same author(s)

1 2 > >>