Open Journal Systems

Implementasi Sistem Monitoring Pertumbuhan Tanaman Sawi Hijau Berbasis Pembelajaran Mesin

       Ade Ramdan

Abstract


Peningkatan produksi di bidang pertanian khususnya sayuran perlu dilakukan dengan memanfaatkan teknologi sejalan dengan meningkatnya kebutuhan masyarakat akan sayuran. Teknologi Artificial Intelligence (AI) dapat mendukung proses bisnis di bidang pertanian yang dapat digunakan untuk meningkatkan hasil produksi pertanian.  Salah satu penggunaan teknologi tersebut adalah dengan mengimplementasikan sistem monitoring pertumbuhan tanaman berbasis pembelajaran mesin. Sistem monitoring tanaman pada masa pertumbuhan tersebut diperlukan guna meningkatkan produksi pertanian. Penelitian ini dilakukan bertujuan untuk merancang sistem monitoring dengan menerapkan algoritma Support Vector Machine (SVM) sebagai classifier dengan metode ekstraksi fitur warna menggunakan metode Hue, Saturation, Intensity  (HIS) pada Raspberry Pi. Hasil penelitian menunjukkan bahwa sistem monitoring pertumbuhan tanaman sawi ini dapat mendeteksi tanaman yang memiliki pertumbuhan bagus dan kurang bagus dengan akurasi 90%.


  http://dx.doi.org/10.31544/jtera.v7.i1.2022.25-30

Keywords


monitoring tanaman; pembelajaran mesin; Raspberry Pi, SVM, HSI

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References


H.D.R. Diah, Ekonomika Agribisnis, Makassar: Perpustakaan Nasional, 2017.

(2021) Kementerian Pertanian website. [Online]. Available: http://rb.pertanian.go.id/?show=page&act=view&id=19

C. Parke, “Impact of Technology on Agriculture and Food Production”, Letterkenny Institute of Technology, 2015.

H.S. Abdullahi, R.E. Sheriff, “Technology Impact on Agricultural Productivity: A Review of Precision Agriculture Using Unmanned Aerial Vehicles”, in Proc. International Conference on Wireless and Satellite Systems (WISATS), Bradford, 2015, pp 388-400.

A. Chlingaryan, S. Sukkarieh, and B. Whelan, “Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review,” Computers and electronics in agriculture, vol. 151, pp. 61–69, 2018.

S. Dimitriadis and C. Goumopoulos, “Applying machine learning to extract new knowledge in precision agriculture applications,” in 2008 Panhellenic Conference on Informatics. IEEE, 2008, pp. 100–104.

R. Varghese and S. Sharma, “Affordable smart farming using iot and machine learning,” in 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2018, pp. 645–650.

M. Kumar, S. Gupta, X.-Z. Gao, and A. Singh, “Plant species recognition using morphological features and adaptive boosting methodology,” IEEE Access, vol. 7, pp. 163 912–163 918, 2019.

U. Shruthi, V. Nagaveni, and B. Raghavendra, “A review on machine learning classification techniques for plant disease detection,” in 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS). IEEE, 2019, pp. 281–284.

R. A. Setyawan, A. Basuki, Ch.Y. Wey, “Machine Vision-Based Urban Farming Growth Monitoring System”, in Proc. Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), Malang, 2020.

K. A. Vakilian and J. Massah, “Design, Development and Performance Evaluation of A Robot to Early Detection of Nitrogen Deficiency in Greenhouse Cucumber (Cucumis Sativus) With Machine Vision”, International Journal of Agriculture, vol. 2, no. 4, pp. 448-454, 2012.

T. W. Saputra, R. E. Masithoh, and B. Achmad, “Development of Plant Growth Monitoring System Using Image Processing Techniques Based on Multiple Images”, in Proc. The 1st International Conference on Tropical Agriculture, 2017, pp 647-653.

A. Ramdan, D. Rianto, I. Sakti, “Pemantau Kondisi Tanaman Pakcoy Berbasis Machine Learning,” in Prosiding Seminar Nasional FisTEK, 2020, pp. 36-41.

R. C. Gonzales, R. E. Woods, “Digital Image Processing”, 3rd Edition, Pearson, 2010.

R. D. Kusumanto, A. N. Tompunu, and W. S. Pambudi, “Klasifikasi Warna Menggunakan Pengolahan Model Warna HSV”, Jurnal Ilmiah Elite Elektro, Vol. 2, No.2, pp. 83-88, 2011.

H. Edha, S. H. Sitorus, and U. Ristian, “Penerapan Metode Transformasi Ruang Warna HUE Saturation Intensity (HIS) Untuk Mendeteksi Kematangan Buah Mangga Harum Manis”, Jurnal Komputer dan Aplikasi, vol. 08, no.1, pp. 1-10, 2020.

W. Zhang, J. Liang, and L. Ren, “Fast Polarimetric Dehazing Method for Visibility Enhancement in HSI Colour Space”, Journal of Optics, 2017.

Y. Guo, Z. Zhang, and H. Yuan, “Single Remote-Sensing Image Dehazing in HSI Color Space”, Journal of The Korean Physical Society, vol. 74, no. 8, pp. 779-784, 2019.

S. Ma, H. Ma, Y. Xu, Sh. Li, Ch. Lv, and M. Zhu, “A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model”, Journal Sensor, 2018.

G. Saravanan, G. Yamuna, and S. Nandhini, “Real Time Implementation of RGB to HSV/HSI/HSL and Its Reverse Color Space Models, in Proc. International Conference on Communication and Signal Processing, 2016.

F. F. Chamasemani and Y. P. Singh, “Multi-class Support Vector Machine (SVM) classifiers – An Application in Hypothyroid detection and Classification”, in Sixth International Conference on Bio-Inspired Computing: Theories and Applications, 2011.

A. S. Ritonga and E. S. Purwaningsih, “Penerapan Metode Support Vector Machine (SVM) Dalam Klasifikasi Kualitas Pengelasan SMAW (Shield Metal ARC Welding), Jurnal Ilmiah Edutic, vol. 5, no. 1, 2018.

A. A. Kasim and M. Sudarsono, “Algoritma Support Vector Machine (SVM) untuk Klasifikasi Ekonomi Penduduk Penerima Bantuan Pemerintah di Kecamatan Simpang Raya Sulawesi Tengah”, in Seminar Nasional APTIKOM (SEMNASTIK), 2019.




DOI: http://dx.doi.org/10.31544/jtera.v7.i1.2022.25-30
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