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

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.