Abstract:
The safety of bulk cargo constitutes a critical aspect of commercial vessel operations,
strictly regulated under the IMSBC Code and the Grain Code. Hygroscopic commodities
such as Sun Flower Meal and Soya Bean Meal are highly susceptible to cargo sweat
phenomena caused by uncontrolled temperature and humidity gradients within the cargo
hold. The primary issue encountered on MV. Pan Flower was that cargo hold condition
monitoring was still conducted manually using rudimentary measuring instruments and
periodic logbook entries, thereby creating data blind spots, particularly during adverse
weather conditions that prevented the crew from physically accessing the main deck area.
This study aimed to design and develop a prototype of a Smart Maritime Cargo
Monitoring System utilizing the Raspberry Pi Pico 2W microcontroller and SHT30
sensors, capable of monitoring cargo hold temperature and humidity autonomously and
in real time. The research employed the Research and Development (R&D) methodology
based on the Borg and Gall model. The test results demonstrated that the system achieved
a mean error rate of 1.28% for temperature and 0.3% for relative humidity, well below
the industrial instrumentation tolerance threshold of 5%. The Store and Forward
algorithm successfully ensured zero data loss under signal blackout conditions, with a
failover transition time of 10,000 ms and resynchronization within 7,200 ms. The multi-
tiered early warning system (SAFE, CAUTION, DANGER) operated responsively
through an acoustic buzzer and Blynk push notifications. Product feasibility validation
yielded scores of 98% from expert lecturers and 97% from Nautical cadets, placing the
prototype in the "Highly Suitable" category, thereby confirming its viability for
implementation as a cargo damage mitigation instrument on bulk carrier vessels.
