Implementasi YOLOv10 untuk Deteksi Kerapatan dan Transparansi Tajuk Pohon melalui Aplikasi Mobile

Authors

  • Alkhadafi Saddam Simparico Universitas Lampung
  • Rico Andrian Universitas Lampung
  • Rahmat Safe'i Universitas Lampung
  • Admi Syarif Universitas Lampung

DOI:

https://doi.org/10.37859/jf.v15i2.9581
Keywords: CNN, kerapatan tajuk, transparansi tajuk, YOLOv10, aplikasi mobile

Abstract

Kerapatan dan transparansi tajuk pohon merupakan indikator penting kesehatan hutan yang berpengaruh terhadap keseimbangan ekosistem dan keanekaragaman hayati. Penelitian ini mengembangkan sistem deteksi real-time berbasis model YOLOv10 yang dioptimalkan untuk perangkat mobile melalui konversi ke TensorFlow Lite, sehingga memungkinkan inferensi cepat dan efisien di lapangan tanpa memerlukan perangkat komputasi besar. Dataset yang digunakan terdiri dari 5.000 citra tajuk pohon yang mencakup sepuluh kelas variasi kerapatan dan transparansi, mewakili lima jenis daun jarum dan lima jenis daun lebar dengan perbedaan morfologi dan karakteristik transmisi cahaya. Pengambilan data dilakukan pada berbagai sudut pandang untuk meningkatkan ketahanan model terhadap kondisi nyata di lapangan. Data dibagi menjadi 70% untuk pelatihan, 10% untuk validasi, dan 20% untuk pengujian. Hasil evaluasi menunjukkan akurasi 97,7% dengan nilai precision, recall, dan F1-score yang tinggi di setiap kelas. Sistem ini berpotensi mempercepat proses survei lapangan, meningkatkan akurasi pemantauan ekosistem, dan menjadi alat pendukung pengambilan keputusan dalam pengelolaan hutan serta program konservasi. Pendekatan ini menawarkan solusi praktis dan terukur untuk pemantauan hutan berkelanjutan dengan memanfaatkan teknologi computer vision mutakhir di perangkat mobile

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References

Safe’i, R. (2018). PENILAIAN VITALITASPOHONJATI DENGANFOREST HEALTH MONITORINGDIKPH BALAPULANG. Jurnal Ecogreen. http://repository.lppm.unila.ac.id/id/eprint/8288

He, L., Zhang, X., Wang, X., Ullah, H., Liu, Y., & Duan, J. (2023). Tree Crown Affects Biomass Allocation and Its Response to Site Conditions and the Density of Platycladus orientalis Linnaeus Plantation. In Forests. mdpi.com. https://www.mdpi.com/1999-4907/14/12/2433

Gao, C., Zhang, Q., Tan, Z., Zhao, G., Gao, S., Kim, E., & Shen, T. (2024). Applying optimized YOLOv8 for heritage conservation: enhanced object detection in Jiangnan traditional private gardens. In Heritage Science (Vol. 12, Issue 1). Springer. https://doi.org/10.1186/s40494-024-01144-1

da Silva, D. de A., de Freitas, E. D. G., Abud, H. F., & Gomes, D. G. (2024). Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds. In AgriEngineering (Vol. 6, Issue 2, pp. 869–880). mdpi.com. https://doi.org/10.3390/agriengineering6020050

Andrian, R., Sriatna, D. A., & Tarigan, F. R. (2024). Classification of Density and Transparency of Needle Leaves Types Using AlexNet and VGG16 Architecture. … Des Systèmes d’ …. https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=16331311&AN=178256174&h=7xxYtu0iTV8yiGaXvZTdhXYdG1qnXH9RTo%2B180w3teT7m60ZyKfs9BB4isl8trQ4vQ4jkwOkHL0wQw%2BOmSZDIw%3D%3D&crl=c

Sofiyana, F., Andrian, R., & Safei, R. (2023). MobileNet untuk Identifikasi Skala Kerapatan dan Transparansi Tajuk Pohon Daun Lebar. KLIK: Kajian Ilmiah Informatika Dan Komputer, 4(3), 1850–1859. http://djournals.com/klik/article/view/1476

Shiri, F. M., Perumal, T., Mustapha, N., & ... (2023). A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU. ArXiv Preprint ArXiv …. https://arxiv.org/abs/2305.17473

Zhu, Y., & Chen, Y. (2023). Adaptive Growth: Real-time CNN Layer Expansion. ArXiv Preprint ArXiv:2309.03049. https://arxiv.org/abs/2309.03049

Liu, Y., Pu, H., & Sun, D. W. (2021). Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices. Trends in Food Science &Technology. https://www.sciencedirect.com/science/article/pii/S0924224421003022

Vijayakumar, A., Vairavasundaram, S., Koilraj, J. A. S., & ... (2024). Real-time visual intelligence for defect detection in pharmaceutical packaging. In Scientific Reports. nature.com. https://www.nature.com/articles/s41598-024-69701-z

Long, J., Wang, X., Zhou, W., Zhang, J., Dai, D., & Zhu, G. (2021). A comprehensive review of signal processing and machine learning technologies for UHF PD detection and diagnosis (I): Preprocessing and localization approaches. IEEE Access. https://ieeexplore.ieee.org/abstract/document/9422716

Yang, Y. (1999). An evaluation of statistical approaches to text categorization. Information Retrieval. https://doi.org/10.1023/A:1009982220290

Maxwell, A. E., Warner, T. A., & Guillén, L. A. (2021). Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—Part 2: Recommendations and best practices. In Remote Sensing. mdpi.com. https://www.mdpi.com/2072-4292/13/13/2591

Dokic, K., Martinovic, M., & ... (2020). Inference speed and quantisation of neural networks with TensorFlow Lite for Microcontrollers framework. 2020 5th South-East …. https://ieeexplore.ieee.org/abstract/document/9221846/

Wasilewski, K., & Zabierowski, W. (2021). A comparison of java, flutter and kotlin/native technologies for sensor data-driven applications. In Sensors. mdpi.com. https://www.mdpi.com/1424-8220/21/10/3324

Zuluaga, C. A., Aristizábal, L. M., Rúa, S., Franco, D. A., & ... (2022). Development of a modular software architecture for underwater vehicles using systems engineering. In Journal of Marine …. mdpi.com. https://www.mdpi.com/2077-1312/10/4/464

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Published

2025-08-19

Issue

Vol. 15 No. 2 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)

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Articles

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