Practical Implementation of AIoT for Optimized Koi Feeding
Authors
Prawit Chumchu1, *, Kailas Patil2, Alfa Nyandoro3
1Kasetsart University, Bangkok, Thailand
2Vishwakarma University, Pune, India
3Regent University, Virginia, USA
*Corresponding author.
Email: prawit@eng.src.ku.ac.th
Corresponding Author
Prawit Chumchu
Available Online 6 January 2026.
- DOI
- 10.2991/978-94-6463-948-3_56How to use a DOI?
- Keywords
- AIoT; deep learning; aquaculture automation; Koi feeding; smart feeding systems
- Abstract
This study presents a practical implementation of the Artificial Intelligence of Things (AIoT) for Koi feeding using low-cost IoT machines powered by Raspberry Pi. To optimize feeding, deep learning is applied in alignment with the widely used five-minute feeding method. YOLOv9-s is employed for shaded area cropping in captured images, while YOLOv5s-CAGSDL detects uneaten pellet clusters with ~99% accuracy. The system was deployed in two Koi ponds, with positive feedback from growers, demonstrating cost-effective automation for aquaculture.
- Copyright
- © 2025 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.
Cite this article
TY - CONF AU - Prawit Chumchu AU - Kailas Patil AU - Alfa Nyandoro PY - 2026 DA - 2026/01/06 TI - Practical Implementation of AIoT for Optimized Koi Feeding BT - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025) PB - Atlantis Press SP - 796 EP - 806 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-948-3_56 DO - 10.2991/978-94-6463-948-3_56 ID - Chumchu2026 ER -