蛋鸡的鸡蛋生产对于蛋鸡养殖业的养殖企业至关重要。然而,目前没有系统或准确的方法来识别商业农场产蛋量低的蛋鸡,这些母鸡中的大多数是由饲养者根据他们的经验识别的。为了解决这个问题,提出了一种适用性广、精度高的方法。首先,饲养员自己将产蛋率低的蛋鸡和普通蛋鸡分开。然后,在卤素灯下,两种不同类型母鸡的高光谱图像是通过高光谱成像设备捕获的。利用顶点成分分析(VCA)算法提取鸡冠冠末端成员谱,得到产蛋低蛋鸡和正常蛋鸡的鸡冠冠谱特征曲线。接下来,采用快速连续小波变换(FCWT)对特征曲线数据进行分析,得到二维光谱特征图像数据集。最后,参考低产蛋母鸡和正常蛋鸡的二维光谱图像数据集,我们开发了一个基于卷积神经网络(CNN)的深度学习模型。当我们使用准备好的数据集测试模型的准确性时,我们发现它的准确率是0.975%。这一结果表明了我们的识别方法,结合了高光谱成像技术,一种FCWT数据分析方法,和CNN深度学习模型,并且在蛋鸡育种植物中非常有效和精确。此外,利用FCWT对高光谱数据进行分析和处理的尝试,将对高光谱技术在其他领域的研究和应用产生重大影响,因为它对数据信号的分析和处理具有高效率和高分辨率的特点。
The egg production of laying hens is crucial to breeding enterprises in the laying hen breeding industry. However, there is currently no systematic or accurate method to identify low-egg-production-laying hens in commercial farms, and the majority of these hens are identified by breeders based on their experience. In order to address this issue, we propose a method that is widely applicable and highly precise. First, breeders themselves separate low-egg-production-laying hens and normal-laying hens. Then, under a halogen lamp, hyperspectral images of the two different types of hens are captured via hyperspectral imaging equipment. The vertex component analysis (VCA) algorithm is used to extract the cockscomb end member spectrum to obtain the cockscomb spectral feature curves of low-egg-production-laying hens and normal ones. Next, fast continuous wavelet transform (FCWT) is employed to analyze the data of the feature curves in order to obtain the two-dimensional spectral feature image dataset. Finally, referring to the two-dimensional spectral image dataset of the low-egg-production-laying hens and normal ones, we developed a deep learning model based on a convolutional neural network (CNN). When we tested the model\'s accuracy by using the prepared dataset, we found that it was 0.975 percent accurate. This outcome demonstrates our identification method, which combines hyperspectral imaging technology, an FCWT data analysis method, and a CNN deep learning model, and is highly effective and precise in laying-hen breeding plants. Furthermore, the attempt to use FCWT for the analysis and processing of hyperspectral data will have a significant impact on the research and application of hyperspectral technology in other fields due to its high efficiency and resolution characteristics for data signal analysis and processing.