IoT and AI for Rural Fishing: A Research for Enhancing Traditional Chinese Fishing Nets

DOI

10.2991/978-2-38476-583-6_15How to use a DOI?

Keywords

Internet of Things (IoT); Artificial Intelligence (AI); Rural fisheries; Precision fishing; Data-driven fishing; Socio-economic upliftment; Chinese fishnet

Abstract

The research here outlines an Internet of Things (IoT) and Artificial Intelligence (AI) research design to increase the efficiency of conventional Chinese fishing nets along Kerala’s coastline. With 2lakh + sonar-based fish records gathered from Chathedam, Cochin Kerala along with various sensor data on water salinity, tidal information (high tide and low tide), wind speed and weather conditions, the study formulates an AI prediction system based on the Random Forest algorithm to predict fish density, while SARIMA/ARIMA used for forecasting the fish trends. The re-search also extends on the architecture designs of integrating the AI predictions to automatic Chinese fishnet pulling electrical winch systems. The dataset contains sonar sensor data with depth, number of fish detected at various intervals, and strength of signals to predict the best time and weather and seasonal impacts. The architecture contains how the dataset is being collected using fish detection sonar devises, processed using the edge computing devices and stored in cloud datastore for artificial intelligent model to run on dataset to predict the outcomes. The Random Forest model, is having 91.3% prediction accuracy, will help to assist sustainable fisheries. The IoT system provides here provides real-time information to fishermen, allowing them to make data-driven decisions in order to increase yield while maintaining healthy marine ecosystems. The research also incorporates Traditional Ecological Knowledge(TEK) in Fisheries and validates AI recommendations against actual fish catch statistics to establish correlation and evaluate the effectiveness of proposed system.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Manoj Krishnan
AU  - R. Karthik
PY  - 2026
DA  - 2026/06/30
TI  - IoT and AI for Rural Fishing: A Research for Enhancing Traditional Chinese Fishing Nets
BT  - Proceedings of the International Conference on Emerging Food Studies: Intersections of Culture, Science and Sustainability (ICEFS 2026)
PB  - Atlantis Press
SP  - 161
EP  - 176
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-583-6_15
DO  - 10.2991/978-2-38476-583-6_15
ID  - Krishnan2026
ER  -