Design of the intelligent feeding machine for largemouth bass on the basis of feeding intensity

Conventional feeders can achieve timed and quantitative feeding, but they cannot optimize feeding strategies on the basis of actual aquaculture conditions. This study evaluated the feeding intensity of largemouth bass and developed an intelligent feeder to achieve efficient and precise feeding. A mobile feeding system was built by designing and simulating the structure of the data acquisition, control, feeding power, storage, and mobile modules of the feeder. The surface water pressure signals during largemouth bass feeding were collected through pressure sensors and analyzed, and the feeding intensity was classified into three levels: strong, weak, and none. Signal features were extracted to construct a dataset and input into five machine learning models for optimal parameter tuning. The precision, recall, F1 score, and average accuracy of the random forest model were 96.2%, 95.5%, 95.6%, and 93.4%, respectively. The YOLOv5 model was adopted to detect remaining feed on the water surface. The feeding system was designed to enable the feeder to automatically track and provide feed into the tank. Experiments were conducted on the intelligent feeding system, with the feed residue rate as the indicator of the practicality of the feeding system. Verification experiments were also performed on eight tanks, and the average feed residue rate was less than 3%, proving that the feeding system has good practicality in actual aquaculture environments.