Research Review on Seaweed Farm Management Using Sensor Technology and Machine Learning

Abstract

Seaweed cultivation is rapidly evolving from a traditional, labor-intensive subsistence activity into a fundamental pillar of the global \"Blue Economy.\" As a sector, it offers essential mechanisms for gigaton-scale carbon sequestration, ecosystem restoration, and sustainable food security for a growing global population. Despite its immense potential, the sector encounters significant scalability impediments due to a reliance on manual monitoring techniques, which are often perilous, sporadic, and reactive rather than proactive. Furthermore, the unpredictability of marine environmental fluctuations—exacerbated by climate change—threatens crop viability through sudden thermal spikes and nutrient flux. This review investigates the transformative paradigm facilitated by the integration of the Internet of Things (IoT) and Machine Learning (ML). Through the synthesis of data from five seminal studies, this paper evaluates the efficacy of autonomous underwater vehicles (AUVs) for structural inspection, multi-spectral remote sensing for bloom tracking, and predictive analytics for growth modeling. The analysis indicates that while seaweed-specific applications remain nascent relative to the mature domains of terrestrial precision agriculture, the transposition of frameworks from finfish aquaculture and general agronomy offers a viable strategic roadmap. Specifically, the convergence of high-resolution sensor data with Deep Learning models promises to enable automated disease pathology recognition and yield optimization, moving the industry toward a data-driven future.

Introduction

Global demand for marine biomass, particularly seaweed, is increasing due to applications in pharmaceuticals, biofuels, bioplastics, and functional foods. Seaweed farming offers a sustainable alternative to land-based agriculture, but industrial-scale aquaculture faces challenges from dynamic, corrosive, and opaque underwater environments. Traditional monitoring by divers is slow and inefficient, leaving farms vulnerable to nutrient depletion, hypoxia, and harmful algal blooms.

Industry 4.0 solutions—including IoT sensors, autonomous underwater vehicles (AUVs), satellites, and UAVs—enable continuous environmental monitoring. Machine Learning (ML) models, such as CNNs and LSTMs, can analyze complex, high-dimensional data to predict growth, detect anomalies, and provide early warnings. The proposed cyber-physical system for precision seaweed farming integrates five layers: sensing, edge processing, data platforms with ML, analytics for actionable insights, and automated control for interventions like depth adjustment or nutrient dosing.

Key insights from literature:

• AUVs improve coverage and reduce human risk but are limited by battery life.

• Satellite and UAV systems allow macro-scale monitoring but are vulnerable to cloud cover.

• Low-power, line-attached sensors enable micro-scale monitoring and early biological stress detection, though real-time communication remains a challenge.

• Transfer learning from terrestrial agriculture ML models shows promise for detecting seaweed diseases and predicting environmental changes.

Challenges and gaps:

• Lack of labeled, annotated datasets hinders supervised ML model training.

• Software integration and intelligent analytics lag behind mature sensing hardware.

Future directions:

1. Develop open-access spectral libraries for seaweed health.

2. Integrate Edge AI to process data locally on AUVs and sensors.

3. Use digital twins to simulate farm environments and optimize operations.

4. Deploy swarm robotics for resilient, scalable monitoring.

Conclusion

The integration of sensor technology and machine learning possesses transformative potential for seaweed farm management, shifting the paradigm from reactive observation to predictive control. While direct research is currently limited, foundational studies by Stenius, Gao, and Xia demonstrate that the components for a \"Smart Seaweed Farm\" are extant. The primary challenge lies in system integration—fusing robotic platforms with intelligent algorithms and overcoming the physical hostility of the marine environment. Addressing the \"Data Gap\" through interdisciplinary collaboration between marine biologists and computer scientists will be paramount. Ultimately, the digitization of seaweed aquaculture is not merely a technological upgrade but a necessary evolution to ensure the scalability of the Blue Economy.

References

[1] I. Stenius et al., \"A system for autonomous seaweed farm inspection with an underwater robot,\" Sensors, vol. 22, no. 13, p. 5064, 2022. [2] J. Gao et al., \"Monitoring seaweed aquaculture in the Yellow Sea with multiple sensors for managing the disaster of macroalgal blooms,\" Remote Sensing of Environment, vol. 231, p. 111209, 2019. [3] C. Xia et al., \"Development of a low-power underwater NFC-enabled sensor device for seaweed monitoring,\" Sensors, vol. 21, no. 14, p. 4649, 2021. [4] K. G. Liakos et al., \"Machine learning in agriculture: A comprehensive updated review,\" Sensors, vol. 21, no. 11, p. 3758, 2021. [5] Y. Chen et al., \"Application of machine learning in intelligent fish aquaculture: A review,\" Aquaculture, vol. 531, p. 735724, 2022.

Copyright

Copyright © 2025 Tamanna Khare, Aditya Sah, Anvita Birje, Megha Gupta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.