Intelligent fish feeding based on machine vision: A review

Feeding is an important link in aquaculture that not only affects the healthy growth of fish but is also the main factor that determines the cost and economic benefits of aquaculture. At present, feeding in aquaculture is mainly based on mechanical timing and quantity, relying on a fixed amount of feed preset by the breeder, which does not take into account the dynamic feeding needs of the fish. Problems of underfeeding or overfeeding may occur, affecting the growth rate of the fish and polluting the aquaculture water. Machine vision is an efficient, economical, non-destructive, and objective detection and analysis technology, which is of great significance in promoting the automation and intelligence of aquaculture. Therefore, the combination of machine vision and fish feeding demand for feeding is helpful to improve efficiency and achieve intelligent aquaculture. This paper reviews the research on intelligent fish feeding based on machine vision. The main factors affecting feeding are introduced, followed by a general description of the process of intelligent fish feeding based on machine vision, and a detailed overview and analysis of the key technologies involved in image acquisition, image processing and analysis, and intelligent fish feeding. It also discusses the challenges and potential solutions in intelligent feeding. In short, it aims to help researchers and industry practitioners to better understand the state of the art of machine vision in intelligent fish feeding, and to assist in promoting accurate feeding and improving the efficiency of aquaculture. & COPY; 2023 IAgrE. Published by Elsevier Ltd. All rights reserved.