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Abstract
Mangrove merupakan ekosistem pesisir yang penting karena berperan sebagai penahan abrasi, penyerap karbon, dan habitat berbagai organisme, namun luasannya terus berkurang akibat aktivitas manusia dan perubahan iklim. Upaya rehabilitasi melalui penanaman kembali sering terkendala oleh rendahnya tingkat kelangsungan hidup bibit mangrove, terutama pada tahap awal pertumbuhan yang rentan terhadap gelombang, arus laut, dan sampah. Untuk mengatasi tantangan tersebut, dikembangkan sistem monitoring bibit mangrove berbasis internet of things dengan dukungan algoritma deteksi objek berbasis kamera dan panel surya. Mikrokontroler digunakan untuk akuisisi data citra dan sensor lingkungan yang kemudian dikirim ke dashboard web melalui protokol komunikasi nirkabel. Hasil analisis menunjukkan bahwa sistem mampu mendeteksi bibit dengan akurasi tinggi serta menjaga keberlanjutan operasi melalui manajemen energi yang efisien. Oleh karena itu, monitoring bibit dapat dilakukan secara berkelanjutan, mandiri, dan ramah lingkungan, sehingga diharapkan dapat meningkatkan efektivitas program rehabilitasi mangrove di berbagai wilayah pesisir.
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Copyright (c) 2025 Kartika
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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References
M. Jia, Z. Wang, and L. Luo, “Global status of mangrove forests in resisting cyclone and tsunami,” The Innovation Geoscience, vol. 1, no. 2, p. 100024, 2023.
X. Chen, Z. Yin, and Z. Li, “Overview on Mangrove Forest Disaster Prevention and Mitigation Functions,” Journal of Ocean University of China, vol. 23, pp. 46–56, 2024.
S. Hülsen, “Mangroves and their ecosystem services are increasingly at risk from tropical cyclones,” Commun Earth Environ, 2025.
D. Reed, “Resilience to Hurricanes Is High in Mangrove Blue Carbon Ecosystems,” Glob Chang Biol, 2025.
P. Menendez and M. W. Beck, “Building Coastal Resilience with Mangroves: The Contribution of Natural Flood Defenses to the Changing Wealth of Nations,” in The Changing Wealth of Nations 2024, World Bank, 2024.
K. Omar, “Mangroves as a nature-based solution: potential impacts of restoration and conservation on coastal resilience,” J Coast Conserv, 2025.
S. Ahmed, “Substantial rehabilitation of mangrove forests along degraded tropical coastlines,” Ecol Eng, 2025.
H. Snyder, “Literature review as a research methodology: An overview and guidelines,” J Bus Res, vol. 104, pp. 333–339, 2019.
G. Li, R. Jian, X. Jun, and G. Shi, “A Review of You Only Look Once Algorithms in Animal Phenotyping Applications,” Animals, vol. 15, no. 8, p. 1126, 2025, doi: 10.3390/ani15081126.
M. Humayun and others, “IoT based environmental monitoring using ThingsBoard,” Sensors, vol. 21, no. 22, p. 7464, 2021.
R. Gupta and others, “Smart agriculture monitoring via IoT platforms,” IEEE Internet Things J, vol. 9, no. 15, pp. 12894–12906, 2022.
A. Hussain, A. Musa, and H. H. Al-Khalidi, “Performance evaluation of MQTT and CoAP in IoT,” in Proceedings of IEEE SmartTech, 2019, pp. 1–6.
D. P. Mishra, S. Bera, and S. Misra, “QoS-aware data transmission in IoT using MQTT,” IEEE Internet Things J, vol. 6, no. 3, pp. 5473–5480, 2019.
H. Sang, Z. Li, X. Shen, S. Wang, and Y. Zhang, “Rapid Identification of Mangrove Leaves Based on Improved YOLOv10 Model,” Forests, vol. 16, no. 7, p. 1068, 2025, doi: 10.3390/f16071068.
J. Moosmann, M. Giordano, C. Vogt, and M. Magno, “TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers,” 2023.
E. Humes, M. Navardi, and T. Mohsenin, “Squeezed Edge YOLO: On-board Object Detection on Edge Devices,” 2023.
A. Chen and others, “YOLOBench: Benchmarking YOLO Models on Embedded and Edge Devices,” 2024.
A. Sharma and others, “Low power consumption analysis of ESP32-CAM in IoT applications,” IEEE Access, vol. 9, pp. 78510–78519, 2021.
B. Liu and others, “Performance of small solar panel for remote IoT devices,” Renewable Energy Journal, vol. 155, pp. 1132–1140, 2020.