Artificial intelligence-based metabolic energy prediction model for animal feed proportioning optimization

With the progress of science and technology, Artificial Intelligence (AI) technology has become one of the mainstream technologies in the current society, providing an important driving force for human development. Thereby, in order to improve the effect of animal feeding, using AI technology to improve animal feed has become a necessary measure. Based on this, this work designs to use Long Short-Term Memory (LSTM) technology to build an intelligent prediction model of metabolic energy, which provides a reference for animal feed proportioning design. This work also explores the comprehensive performance of the LSTM model through simulation evaluation. The model is evaluated with different nodes as the main indicators. The results show that compared with the models with 5 and 20 nodes, the model with 10 nodes has better performance, and the highest data calculation accuracy of the model is about 90%. Meanwhile, the highest fitting degree of the model designed is 98.2%, and the lowest is 96.2%. It suggests that the model designed can better predict metabolic energy. This work provides technical support for expanding the application scope of AI technology and contributes to the intelligence of animal feeding.

Automated summary

With the advancement of science and technology, Artificial Intelligence (AI) has become a mainstream technology in society, playing an important role in human development. In order to improve animal feeding, the use of AI technology to enhance animal feed has become necessary. This study utilizes Long Short-Term Memory (LSTM) technology to construct an intelligent prediction model for metabolic energy, providing guidance for animal feed formulation. The LSTM model's performance is evaluated through simulation and shows that the model with 10 nodes has the best performance, achieving a data calculation accuracy of approximately 90%. The model also exhibits high fitting degrees, ranging from 96.2% to 98.2%, indicating its ability to accurately predict metabolic energy. This work provides technical support for expanding the application of AI technology and contributes to the automation of animal feeding.