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 $200 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 fillets in a non-invasive, objective, and high-throughput manner. In this study, 250 raw chicken breast fillet 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.

本研究解决了全球禽类产业中，由于快速生长选育导致的 woody breast 问题，该问题造成每年高达2亿美元的经济损失。通过采用神经网络架构搜索（NAS）优化的宽深学习模型（NAS-WD），该研究实现了85% 的 woody breast 分类准确率，并建立了与样本硬度数据相关的回归模型，显著提高了传统方法的效能。

Neural Network Architecture Search Enabled Wide-Deep Learning (NAS-WD)
  for Spatially Heterogenous Property Awared Chicken Woody Breast
  Classification and Hardness Regression

神经网络架构搜索促进的宽深学习（NAS-WD）用于空间异质性属性感知的鸡肉 woody breast 分类与硬度回归