An Autonomous Cyber-Physical System for Mangrove Monitoring and Active Rehabilitation in Coastal Ecosystems

Abstract

Mangrove forests are ecologically critical for coastal defense, biodiversity, and blue carbon sequestration, yet they face escalating pressure from climate change, salinity intrusion, and anthropogenic activity. Restoration and monitoring remain difficult because the terrain is waterlogged and hazardous, and because remote sensing cannot resolve the sub-canopy, ground-level parameters that govern seedling survival. This paper presents the design of the Mangrove Autonomous Cyber-Physical System (MACS), a proposed robotic platform integrating a six-wheel low-ground-pressure chassis, multi-parameter IoT sensing, 2D LiDAR occupancy-grid SLAM, and a machine learning plantation-suitability architecture, powered by a hybrid solar–Li-ion system. This study's contribution is a complete and internally consistent energy, throughput, and cost model for ground-based mangrove rehabilitation. Under stated assumptions, the design projects an endurance of 7.0 hours at a 12.55 W average draw, a daily traverse of ≈1.5 km covering ≈1.8 ha of mapped area, a throughput of ≈28 seedlings per operating day, and a hardware-amortized cost of $0.22 per seedling on a $1,100 build. This study further specifies a validation protocol defining precisely what must be measured to substantiate each value. Prototype construction and field validation are identified as immediate future work.