A prediction method for transport stress in meat sheep based on GA-BPNN.

Transportation of live sheep is a relatively important part of the meat sheep industry, and the generation of transportation stress seriously threatens the organism health of live sheep as well as the quality of lamb meat. In this study, we selected Lake sheep, one of the edible sheep breeds, as the meat sheep research object, analyzed the actual needs of meat sheep transportation bio-sensing and modeling evaluation, and built a systematic bio-signal detection, processing and modeling method from bio-signal sensing, core waveform extraction to feature parameter estimation using wearable meat sheep physiological dynamic sensing and continuous monitoring of transportation environment sensors, and used modern spectrum estimation methods to judge and strip the target signal waveform from it to achieve accurate sensing and acquisition of transport key parameters. We also use modern spectral estimation methods to determine and extract the target signal waveforms from them, and achieve accurate sensing and acquisition of transport key parameters. Based on this, a qualitative stress assessment method based on external performance and a graded prediction model for transportation stress of meat sheep are constructed, and a predictive analysis and estimation management mechanism of stress level based on genetic algorithm optimized reverse artificial neural network is realized. Using a qualitative stress assessment method based on external manifestations, we established stage-specific classification rules for monitoring data in the transportation environment of meat sheep.The artificial neural network with optimized parameters based on the classification rules applying genetic algorithm is designed and implemented for real-time and efficient stress level prediction management mechanism for meat sheep transportation. The graded prediction model of meat sheep transportation stress was established by GA-BPNN and compared with the BP neural network optimized by particle swarm algorithm, simulated annealing algorithm and ant colony algorithm. The best optimization effect of GA was obtained, and the average prediction accuracy of the model could reach 89.81%. Finally, it achieves to improve the transportation reliability, reduce the transportation risk, and solve the problems of inefficient meat sheep transportation supervision and quality control.