Penerapan Algoritma Yolov3 pada Sistem Cerdas Pendeteksi dan Pengendali Hama Bawang Merah Berbasis IoT
- Avif As'ad Politeknik Negeri Sriwijaya, Palembang, Indonesia
-
Suroso Suroso
*
Universitas Muhammadiyah Palembang,
Indonesia
- Ciksadan Ciksadan Universitas Muhammadiyah Palembang, Indonesia
- Erni Hawayanti Universitas Muhammadiyah Palembang, Indonesia (*) Corresponding Author
Abstract
Technological advancements play a crucial role in enhancing the efficiency of modern agriculture, particularly in addressing pest management challenges. This study focuses on the development of an automatic pest detection system for shallot crops using a combination of Arduino Uno microcontroller, ESP32-CAM camera module, and YOLOv3 object detection model. The system is designed to detect pests in real-time through images captured by ESP32-CAM and analyzed using YOLOv3, then provide an automatic response by spraying pesticides only in areas where pests are detected. The study began with the development of hardware and software for the automatic pest detection system. Arduino Uno is used as the main microcontroller to control the entire system, while ESP32-CAM is responsible for capturing images and detecting pests. The YOLOv3 model is trained using the COCO dataset, supplemented with sample images of pests on shallot crops to improve detection accuracy. The training process is conducted using a GPU to speed up model learning. Field tests on shallot crops infested with various types of pests show that this system has a high accuracy rate in detecting pests and effectively provides automatic pesticide spraying responses. The spraying system's effectiveness reaches 93%, ensuring pesticides are sprayed only in areas where pests are detected, thus optimizing pesticide use and reducing negative environmental impacts. This system offers an efficient and environmentally friendly solution for pest control and has significant potential for application in various agricultural scenarios. This research contributes to the improvement of agricultural productivity and the welfare of farmers in Indonesia.
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References
A. H. N. Hidayah, S. Ahmad Radzi, N. A. Razak, W. H. M. Saad, Y. C. Wong, and A. A. Naja, “Disease Detection of Solanaceous Crops Using Deep Learning for Robot Vision,” Journal of Robotics and Control (JRC), vol. 3, no. 6, pp. 790–799, Dec. 2022, doi: 10.18196/jrc.v3i6.15948.
L. C. Ngugi, M. Abelwahab, and M. Abo-Zahhad, “Recent advances in image processing techniques for automated leaf pest and disease recognition – A review,” Information Processing in Agriculture, vol. 8, no. 1, pp. 27–51, Mar. 2021, doi: 10.1016/j.inpa.2020.04.004.
L. D. Utami, L. Yusuf, and D. Nurlaela, “Komparasi Algoritma Naïve Bayes dan Support Vectors Machine pada Analisis Sentimen SMS HAM dan SPAM,” Infotek : Jurnal Informatika dan Teknologi, vol. 4, no. 2, pp. 249–258, Jul. 2021, doi: 10.29408/jit.v4i2.3665.
N. M. Yasen, S. Rifka, R. Vitria, and Y. Yulindon, “Pemanfaatan Yolo Untuk Deteksi Hama Dan Penyakit Pada Daun Cabai Menggunakan Metode Deep Learning,” Elektron : Jurnal Ilmiah, pp. 63–71, Dec. 2023, doi: 10.30630/eji.0.0.397.
I. K. D. A. Oka, B. P. W. Nirmala, and M. A. P. Putra, “Model IoT Berbasis Fuzzy Tsukamoto Untuk Penyemprotan Pestisida Otomatis Pada Tanaman Sayur Kubis,” Progresif: Jurnal Ilmiah Komputer, vol. 18, no. 2, p. 141, Jul. 2022, doi: 10.35889/progresif.v18i2.923.
A. Dharmawan and S. Soekarno, “UJI DISTRIBUSI SEMPROTAN SPRAYER PESTISIDA DENGAN PATTERNATOR BERBASIS WATER LEVEL DETECTOR,” Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering), vol. 9, no. 2, p. 85, Jun. 2020, doi: 10.23960/jtep-l.v9i2.85-95.
I. N. Yulita, M. F. R. Rambe, A. Sholahuddin, and A. S. Prabuwono, “A Convolutional Neural Network Algorithm for Pest Detection Using GoogleNet,” AgriEngineering, vol. 5, no. 4, pp. 2366–2380, Dec. 2023, doi: 10.3390/agriengineering5040145.
A. Y. Raj, A. Venkatraman, A. Vinodh, and H. Kumar, “Autonomous Drone for Smart Monitoring of an Agricultural Field,” in 2021 7th International Engineering Conference “Research & Innovation amid Global Pandemic" (IEC), IEEE, Feb. 2021, pp. 211–212. doi: 10.1109/IEC52205.2021.9476097.
M. G. Nayagam, B. Vijayalakshmi, K. Somasundaram, M. A. Mukunthan, C. A. Yogaraja, and P. Partheeban, “Control of pests and diseases in plants using IOT Technology,” Measurement: Sensors, vol. 26, p. 100713, Apr. 2023, doi: 10.1016/j.measen.2023.100713.
M. M. U. Saleheen, M. S. Islam, R. Fahad, M. J. B. Belal, and R. Khan, “IoT-Based Smart Agriculture Monitoring System,” in 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), IEEE, Sep. 2022, pp. 1–6. doi: 10.1109/IICAIET55139.2022.9936826.
M. Eng. , S. Ula, S. Susilo, E. Listijorini, R. Irawan, A. Sambas, and A. T. Assidiq, “Spraying Agricultural Crops Based on Internet of Things (IoT),” FLYWHEEL : Jurnal Teknik Mesin Untirta, p. 20, Dec. 2021, doi: 10.36055/fwl.v0i0.12847.
X. Yang et al., “A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 2, pp. 273–302, Feb. 2021, doi: 10.1109/JAS.2020.1003536.
M. R. M. Kassim, “IoT Applications in Smart Agriculture: Issues and Challenges,” in 2020 IEEE Conference on Open Systems (ICOS), IEEE, Nov. 2020, pp. 19–24. doi: 10.1109/ICOS50156.2020.9293672.
M. I. K. Saraan and R. F. A. K. Rambe, “Kebijakan Pengembangan Inovasi Teknologi Pertanian Presisi di Provinsi Sumatera Utara,” Jurnal Kajian Agraria dan Kedaulatan Pangan (JKAKP), vol. 2, no. 1, pp. 1–5, Aug. 2023, doi: 10.32734/jkakp.v2i1.13319.
F. Suryatini, M. Maimunah, and F. I. Fauzandi, “Implementasi Sistem Kontrol Irigasi Tetes Menggunakan Konsep IoT Berbasis Logika Fuzzy Takagi-Sugeno,” JTERA (Jurnal Teknologi Rekayasa), vol. 4, no. 1, p. 115, Jun. 2019, doi: 10.31544/jtera.v4.i1.2019.115-124.
J. R. Ziliwu, G. C. Setyawan, and H. Budiati, “Penerapan ESP32-CAM dan TinyML dalam Klasifikasi Gambar Buah dan Sayuran,” Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi, vol. 13, no. 1, p. 584, Apr. 2024, doi: 10.35889/jutisi.v13i1.1869.
C.-J. Chen, Y.-Y. Huang, Y.-S. Li, C.-Y. Chang, and Y.-M. Huang, “An AIoT Based Smart Agricultural System for Pests Detection,” IEEE Access, vol. 8, pp. 180750–180761, 2020, doi: 10.1109/ACCESS.2020.3024891.
W. Pebrianto, P. Mudjirahardjo, and S. H. Pramono, “YOLO Method Analysis and Comparison for Real-Time Human Face Detection,” in 2022 11th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), IEEE, Aug. 2022, pp. 333–338. doi: 10.1109/EECCIS54468.2022.9902919.
G. P. Borikar, C. Gharat, and S. R. Deshmukh, “Application of Drone Systems for Spraying Pesticides in Advanced Agriculture: A Review,” IOP Conf Ser Mater Sci Eng, vol. 1259, no. 1, p. 012015, Oct. 2022, doi: 10.1088/1757-899X/1259/1/012015.
S. Rahmah and H. Hendri, “Sistem Pendeteksi Ketinggian Air Menggunakan Pompa Berpenggerak Motor BLDC Berbasis Mikrokontroller,” JTEV (Jurnal Teknik Elektro dan Vokasional), vol. 6, no. 1, p. 286, Feb. 2020, doi: 10.24036/jtev.v6i1.107973.
Miftahul Walid, H. Hoiriyah, and A. Fikri, “PENGEMBANGAN SISTEM IRIGASI PERTANIAN BERBASIS INTERNET OF THINGS (IoT),” Jurnal Mnemonic, vol. 5, no. 1, pp. 31–38, Jan. 2022, doi: 10.36040/mnemonic.v5i1.4452.
P. B. and M. Akila, “IoT-based pest detection and classification using deep features with enhanced deep learning strategies,” Eng Appl Artif Intell, vol. 121, p. 105985, May 2023, doi: 10.1016/j.engappai.2023.105985.
T. Suciaty and Y. R. Hidayat, “Analisis Efisiensi Ekonomi Penggunaan Faktor-Faktor Produksi pada Usahatani Kedelai (Glycine Max L. Merrill) (Studi Kasus di Desa Bantarwaru Kecamatan Gantar Kabupaten Indramayu),” Jurnal Ekonomi Pertanian dan Agribisnis, vol. 3, no. 4, pp. 662–670, Jan. 2019, doi: 10.21776/ub.jepa.2019.003.04.1.
D. Popescu, A. Dinca, L. Ichim, and N. Angelescu, “New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review,” Front Plant Sci, vol. 14, Nov. 2023, doi: 10.3389/fpls.2023.1268167.
C. CHAIRUNNISA, E. E. MASYURA, D. AIDILA, M. R. MARKUASA SIAGIAN, N. NANDA, and S. RAHMADANI, “FAKTOR PENYEBAB DAN DAMPAK PAPARAN PESTISIDA TERHADAP KESEHATAN PETANI,” Journal of Nursing and Public Health, vol. 11, no. 2, pp. 331–337, Oct. 2023, doi: 10.37676/jnph.v11i2.5094.
K. MURUGAN, B. J. SHANKAR, A. SUMANTH, C. V. SUDHARSHAN, and G. V. REDDY, “Smart Automated Pesticide Spraying Bot,” in 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), IEEE, Dec. 2020, pp. 864–868. doi: 10.1109/ICISS49785.2020.9316063.
R. D. Septian, L. Afifah, T. Surjana, N. W. Saputro, and U. Enri, “Identifikasi dan Efektivitas Berbagai Teknik Pengendalian Hama Baru Ulat Grayak Spodoptera frugiperda J. E. Smith pada Tanaman Jagung berbasis PHT- Biointensif,” Jurnal Ilmu Pertanian Indonesia, vol. 26, no. 4, pp. 521–529, Oct. 2021, doi: 10.18343/jipi.26.4.521.
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Copyright (c) 2024 Avif As'ad, Suroso Suroso, Ciksadan Ciksadan, Erni Hawayanti
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