Classification of Leftover Shrimp Feed Based on Lift Net Design Utilizing the k-Nearest Neighbors Algorithm

Ahmad Hannan Asy-Syaf’ie; Agus Indra Gunawan; Setiawardhana; Muhammad Edy Hidayat; Muhammad Andi Kurniawan

doi:10.46604/aiti.2025.15095

Authors

Ahmad Hannan Asy-Syaf’ie Department of Electrical Engineering, Politeknik Electronika Negeri Surabaya, Surabaya, Indonesia
Agus Indra Gunawan Department of Electrical Engineering, Politeknik Electronika Negeri Surabaya, Surabaya, Indonesia
Setiawardhana Department of Informatic and Computer Engineering, Politeknik Electronika Negeri Surabaya, Surabaya, Indonesia
Muhammad Edy Hidayat Departement of Mechatronics Engineering, Politeknik Bosowa, Makassar, Indonesia
Muhammad Andi Kurniawan PT. Lumbung Nusantara Nastiti, Lampung, Indonesia

DOI:

https://doi.org/10.46604/aiti.2025.15095

Keywords:

feeding management, ultrasonic, k-NN, shrimp, aquaculture

Abstract

An effective Feeding Management System (FMS) is crucial in shrimp farming, as both overfeeding and underfeeding can adversely affect shrimp growth. To ensure optimal nutrition, an accurate FMS must account for factors such as shrimp size, weight, age, and leftover feed. This study presents a method for detecting leftover shrimp feed using custom-designed lift nets equipped with paired ultrasonic sensors. Two critical aspects are examined: the optimal timing for measurement and the ideal placement of the transmitter. Results show that measurements should be taken within 10 minutes of feed immersion to avoid feed disintegration. Additionally, placing the transmitter on the outer side of the lift net improves measurement accuracy. Ultrasonic echoes are analyzed to classify leftover feed using the k-Nearest Neighbors algorithm. Root Mean Square voltage-based classification effectively groups leftover feed into five classes, highlighting its potential to improve aquaculture feed management.

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