Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years,the global poultry industry has faced a challenging problem in the form of woody breast(WB)conditions.This condition ha...Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years,the global poultry industry has faced a challenging problem in the form of woody breast(WB)conditions.This condition has caused significant economic losses as high as s200 million annually,and the root cause of WB has yet to be identified.Human palpation is the most common method of distinguishing a WB from others.However,this method is time-consuming and subjective.Hyperspectral imaging(HSI)combined with machine learning algorithms can evaluate the WB conditions of fllets in a non-invasive,objective,and high-throughput manner.In this study,250 raw chicken breast fllet samples(normal,mild,severe)were taken,and spatially heterogeneous hardness distribution was first considered when designing HSI processing models.The study not only classified the WB levels from HSI but also built a regression model to correlate the spectral information with sample hardness data.To achieve a satisfactory classification and regression model,a neural network architecture search(NAS)enabled a wide-deep neural network model named NAS-WD,which was developed.In NAS-WD,NAS was first used to automatically optimize the network architecture and hyperparameters.The classification results show that NAS-WD can classify the three WB levels with an overall accuracy of 95%,outperforming the traditional machine learning model,and the regression correlation between the spectral data and hardness was 0.75,which performs significantly better than traditional regression models.展开更多
基金support by the University of Arkansas Experimental Station and the University of Arkansas College of Engineering,USDA National Institute of Food and Agriculture (No:2023-70442-39232,2024-67022-42882).
文摘Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years,the global poultry industry has faced a challenging problem in the form of woody breast(WB)conditions.This condition has caused significant economic losses as high as s200 million annually,and the root cause of WB has yet to be identified.Human palpation is the most common method of distinguishing a WB from others.However,this method is time-consuming and subjective.Hyperspectral imaging(HSI)combined with machine learning algorithms can evaluate the WB conditions of fllets in a non-invasive,objective,and high-throughput manner.In this study,250 raw chicken breast fllet samples(normal,mild,severe)were taken,and spatially heterogeneous hardness distribution was first considered when designing HSI processing models.The study not only classified the WB levels from HSI but also built a regression model to correlate the spectral information with sample hardness data.To achieve a satisfactory classification and regression model,a neural network architecture search(NAS)enabled a wide-deep neural network model named NAS-WD,which was developed.In NAS-WD,NAS was first used to automatically optimize the network architecture and hyperparameters.The classification results show that NAS-WD can classify the three WB levels with an overall accuracy of 95%,outperforming the traditional machine learning model,and the regression correlation between the spectral data and hardness was 0.75,which performs significantly better than traditional regression models.
