Pemanfaatan Pengolahan Citra Untuk Deteksi dan Identifikasi Hama pada Tanaman Secara Otomatis

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Siska Julyani
Khairullah Khairullah

Abstract

Technological advances in modern agriculture have driven the development of automated systems to improve efficiency and accuracy in monitoring crop conditions. One promising approach is the use of digital image processing to automatically detect crop conditions. This study aims to examine image processing methods and algorithms that can be used in identifying various aspects of crops, such as pest and disease detection, determining maturity levels, and evaluating leaf and stem health. By using techniques such as image segmentation, feature extraction, and machine learning-based classification, this system is able to analyze crop images in real-time with a high degree of accuracy. The results of the study indicate that image processing has great potential in supporting an efficient, environmentally friendly, and sustainable precision farming system. The implementation of this technology is expected to help farmers in making faster and more precise decisions, thereby increasing the productivity and quality of agricultural products

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How to Cite
Julyani, S. and Khairullah, K. (2025) “Pemanfaatan Pengolahan Citra Untuk Deteksi dan Identifikasi Hama pada Tanaman Secara Otomatis”, Ranah Research : Journal of Multidisciplinary Research and Development, 7(5), pp. 3518-3525. doi: 10.38035/rrj.v7i5.1661.

References

Djafarudin. (2004). Dasar-dasar pengendalian penyakit tanaman. Jakarta: Bumi Aksara.
Ge, Y., Liu, Z., Chen, J., & Sun, T. (2014). Estimation of paddy rice leaf area index using digital photography. Proceedings - 2014 7th International Congress on Image and Signal Processing, CISP 2014, 681–686. https://doi.org/10.1109/CISP.2014.7003865
Hasan, A., Widodo, Mutaqin, K. H., Taufik, M., & Hidayat, S. H. (2021a). Single image-NDVI method for early detection of mosaic symptoms in Capsicum annuum. Jurnal Fitopatologi Indonesia, 17(1), 10.14692/jfi.17.1. 9–18. https://doi.org/
Hasan, A., Widodo, Mutaqin, K. H., Taufik, M., & Hidayat, S. H. (2021b). Quantitative assessment of mosaic disease severity based on digital image processing. IOP Conference Series: Earth and Environmental Science, 694, 1–9. https://doi.org/10.1088/1755-1315/694/1/ 012043
Hasan, A., Widodo, Mutaqin, K. H., Taufik, M., & Hidayat, S. H. (2022). Characteristics of virus symptoms in chili plants (Capsicum frutescens) based on RGB image analysis. Agrivita, 44(3), 459–469. https://doi.org/10.17503/agrivita. v41i0.3731
Alami, Tegar. Yeni Hendriyeni. dkk. 2023. “Kecerdasan Buatan Untuk Monitoring Haman dan Penyakit pada Tanaman: Systematic Literature Review”. Jurnal Ilmu Komputer Agri-Informatika: Institut Pertanian Bogor. diakses pada tanggal 10 Mei 2025 http://journal.ipb.ac.id/index.php/jika
Adnan. Mira Landep. dkk. 2015. “Identifikasi Varietas Padi Menggunakan Pengelolaan Citra Digital dan Analisis Diskriminan”. Jurnal Balai Pengkajian Teknologi Pertanian Papua: Merauke Papua. diakses pada tanggal 10 Mei 2025 http://journal.adnan.et.al.ac.id//pengelolaan.citra/
Sheila, syenira. Anwar, khalil. dkk. 2023. “Deteksi Penyakit Daun Padi Berbasis Pengelolaan Citra Menggunakan Metode Convolutional Neural Network (CNN)”. Jurnal Sistem Teknologi Informasi: Universitas Negeri Jakarta. diakses pada 10 Mei 2025 http://jounal.unj.ac.id.penyakitdaunberbasiscitra/
Landep, Mira. Wahyuni, sri. dkk. 2020. “Identifikasi Klasifikasi Morfologi Benih Varietas Unggul Padi Menggunakan analisis citra digital”. Jurnal Balai Besar Penelitian Tanaman Padi: Jawa Barat Indonesia. diakses pada 10 Mei 2025 https://dx.doi.org/10.2108/jpptp.v4n1.2020.p27-34
Hasan, Asmar. 2024.”Analisis Tingkat Kesehatan Tanaman Padi Sawah Berbasis Pengolahan Citra Digital.” Jurnal Agroteknos: Fakultas Pertanian Universitas Halu Oleo, Kendari. diakses pada 10 Mei 2025 http://journalagroteknos.vol.14.no1.hal1-8/
Narmadha, R. P., & Arulvadivu, G. (2017). Detection and measurement of paddy leaf disease symptoms using image processing. 2017 International Conference on Computer Communication and Informatics, ICCCI 2017, 5 8. https://doi.org/10.1109/ICCCI.2017. 8117730.
Nio, S. A. & Yunia, B. 2011. Konsentrasi Klorofil Daun Sebagai Indikator Kekurangan Air Pada Tanaman. Jurnal Ilmiah Sains, 11(2), 167-173.
Eddy Tri Sucianto, M. A. (2021). di Sistem Polikultur. Jurnal Pendidikan Biologi & Biologi, 13(2), 158–168.
Klasifikasi Ciri Gray Level Co Occurance Matrix Segmentation of K-Means Image of Tin Leaves with Characteristic Classification of Gray Level Co Occurance Matrix. 9(2), 223–233. https://doi.org/10.26418/justin.v9i2.44139
Ramadhan, R. P., Marpaung, N. L., Informatika, D. T., Teknik, L., Universitas, E., Teknik, F., & Riau, U. (2019). Identifikasi jenis penyakit daun tanaman jagung menggunakan jaringan saraf tiruan berbasis backpropagation [1]. 6, 1–5.
Badan Pusat Statistik, “Luas Panen dan Produksi Padi di Indonesia 2021,” Badan Pusat Statistik, Jul. 12, 2022. https://www.bps.go.id/publication/2022/07/1 2/c52d5cebe530c363d0ea4198/luas-panen danproduksi-padi-di-indonesia-2021.html (accessed Oct. 22, 2022).
Walascha, A. Febriana, D. Saputri, D. Sri, N. Haryanti, and R. Tsania, “Review Artikel : Inventarisasi Jenis Penyakit yang Menyerang Daun Tanaman Padi ( Oryza sativa L .),” pp. 471–477, 2021.
S. Simbolon, N. I. Pangaribuan, and N. M. Aruan, “Analisis Sentimen Aplikasi E Learning Selama Pandemi Covid-19 Dengan Menggunakan Metode Support Vector Machine Dan Convolutional Neural Network,” Seminastika, vol. 3, no. 1, pp. 16 25, 2021, doi: 10.47002/seminastika.v3i1.236.
M. F. Naufal, “Comparative Analysis of Image Classification Algorithm for,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 2, pp. 311–318, 2021, doi: 10.25126/jtiik.202184553.
H. Hermanto, A. Mustopa, and A. Y. Kuntoro, “Algoritma Klasifikasi Naive Bayes Dan Support Vector Machine Dalam Layanan Komplain Mahasiswa,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 2, pp. 211–220, 2020, doi: 10.33480/jitk.v5i2.1181.