Abstract
The success of fish farming will depend on improved feed management and lower operating costs, which are essential factors in facilitating an efficient food allocation to the fish. Various automatic fish feeders were used to feed the fish at set intervals, it consists of a mechanical and electrical system to form a device and execute a programmed method, instead of manually feeding the fish by hand. Many methods are effective at evaluating and quantifying fish feeding intensity but are mostly done on the movement and behavior of the fish. However, recognition accuracy is affected due to water quality and the overlapping of fish. To solve this problem, in this study, the author will be focusing on the model in counting the fish pellets, it will capture the image, and count the pellets and the results will be a novel method as a basis for releasing pellets in laying the logical foundation in creating a modern real-time smart fish feeder. The model produced a significant result that detected small objects like fish pellet and count that has gathered a minimal loss in terms of classification, localization, regularization, and normalization.
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