Intelligent detection and control methods for feed surplus in livestock farms are summarized and evaluated in this paper. Increasing scale and intensification render traditional feeding management inadequate for modern efficiency, welfare, and sustainability requirements. Intelligent and precise feeding management has become a key direction for industry transformation and upgrading. Traditional manual inspection decisions are plagued by high labour intensity, poor timeliness, and low precision. They not only increase production costs but also trigger animal health problems and environmental pollution. These issues hinder optimal feed resource allocation. In this research, the intelligent detection and control methods for feed surplus in livestock farms were summarized and evaluated. The applications of sensor fusion, machine vision, and intelligent inspection robots in feed surplus detection, and the principles, advantages, limitations of different detection technologies were analyzed. The types, structural designs, and performance characteristics of automatic feeding actuators were discussed. The current state of farm decision-making systems and preliminary IoT system construction were briefly summarized, with emphasis on various models and methods applied in farm systems. Current limitations were generalized and future research directions including multimodal fusion, edge intelligence, and lightweight models were proposed.
Zhou et al. (Mon,) studied this question.