PENGEMBANGAN PLATFORM DIGITAL DETEKSI GULMA CITRA PADA LAHAN PERTANIAN BERBASIS YOLOV5
Abstract
Gulma merupakan salah satu penyebab menurunnya produktivitas pertanian karena bersaing dengan tanaman budidaya dalam memperoleh unsur hara, air, dan cahaya. Identifikasi gulma secara manual dinilai kurang efisien dan rentan terhadap kesalahan. Penelitian ini bertujuan membangun sistem deteksi gulma pada lahan pertanian menggunakan algoritma YOLOv5 berbasis pengolahan citra digital. Dataset diperoleh melalui Roboflow, kemudian model dilatih menggunakan Google Colab dengan dukungan GPU dan diuji pada lingkungan lokal. Hasil penelitian menunjukkan bahwa sistem mampu mendeteksi gulma secara otomatis dengan menampilkan bounding box pada objek yang teridentifikasi. Sistem ini diharapkan dapat membantu meningkatkan efisiensi dan akurasi pemantauan lahan pertanian
References
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[3] J. Tahsinia, O. Arifudin, dan T. Ibrahim, “Pengembangan Sistem Informasi Manajemen dalam Dunia Pendidikan,” Jurnal Pendidikan, vol. 5, no. 6, pp. 966–977, 2024.
[4] W. U. Harahap dan W. Fadhillah, “Identification of Weed Phenological Changes After Spraying Various Types and Dosages of Active Herbicide,” Journal of Agrotechnology, vol. 25, no. 2, pp. 116–121, 2022.
[5] [5] N. Sujana, M. M. Mutoffar, dan A. A. Haryanto, “Pemanfaatan Roboflow untuk Deteksi Objek Berbasis Computer Vision,” Jurnal Teknologi Informasi, vol. 6, no. 2, pp. 115–124, 2024.
[6] J. Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788, 2016.
[7] G. Jocher et al., “YOLOv5: State-of-the-Art Object Detection Models,” arXiv preprint arXiv:2104.10934, 2023.
[8] A. Kamilaris dan F. X. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, 2022.
[9] M. Hasan et al., “Weed Detection Using Deep Learning Techniques: A Review,” IEEE Access, vol. 10, pp. 102345–102360, 2022.
[10] S. Sladojevic et al., “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification,” Computational Intelligence and Neuroscience, pp. 1–11, 2021.
[11] R. Mohanty, D. Hughes, dan M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Frontiers in Plant Science, vol. 10, pp. 1–10, 2020.
[12] T. Lin et al., “Microsoft COCO: Common Objects in Context,” European Conference on Computer Vision, pp. 740–755, 2014.
[13] A. Bochkovskiy, C. Y. Wang, dan H. Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020.
[14] M. Everingham et al., “The Pascal Visual Object Classes Challenge,” International Journal of Computer Vision, vol. 88, no. 2, pp. 303–338, 2019.
[15] P. P. Singh dan R. Singh, “Precision Agriculture Using Machine Learning and Computer Vision,” International Journal of Agricultural Engineering, vol. 12, no. 1, pp. 45–55, 2023.
[16] M. Marsujitullah, D. A. Kaligis, and F. X. Manggau, “Health Analysis of Rice Plants Based on the Normalized Difference Vegetation Index (NDVI) Value in Image of Unmanned Aircraft (Case Study of Merauke - Papua Selatan)”, etj, vol. 8, no. 2, pp. 1986–1991, Feb. 2023.
[17] Saliki, Marsujitullah, and Suyadi, “Land Contour Identification Study in Rice Field Areas in Merauke: Geospatial Approach”, etj, vol. 10, no. 6, pp. 5487–5493, Jun. 2025.
[18] Saliki, Marsujitullah, and Suyadi, “Spatial Data Handling System in The Nasai Village Area, South Papua”, etj, vol. 9, no. 6, pp. 4211–4216, Jun. 2024.
[19] Saliki, Marsujitullah, and Suyadi, “Utilization of Public Space in Market Area Wamanggu Case Study Merauke”, etj, vol. 9, no. 12, pp. 5600–5605, Dec. 2024
Published
2025-08-31
How to Cite
Marsujitullah_Marwan, Rahangmetan, K. A., & Chotimah, C. (2025). PENGEMBANGAN PLATFORM DIGITAL DETEKSI GULMA CITRA PADA LAHAN PERTANIAN BERBASIS YOLOV5. MUSTEK ANIM HA, 14(02), 68 - 73. https://doi.org/10.35724/mustek.v14i02.7406
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Article
