Zhejiang Key Laboratory of Large-Scale Integrated Circuit Design, Hangzhou Dianzi University, Hangzhou 310018, China.
Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, School of Physics and Photoelectric Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
Sensors (Basel). 2023 Mar 31;23(7):3645. doi: 10.3390/s23073645.
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.
蛋鸡的产蛋量对蛋鸡养殖行业的养殖企业至关重要。然而，目前商业农场中没有系统或准确的方法来识别低产蛋鸡，这些鸡大多是由饲养员根据经验来识别的。为了解决这个问题，我们提出了一种广泛适用且精度高的方法。首先，饲养员自己将低产蛋鸡和正常产蛋鸡分开。然后，在卤素灯下，通过高光谱成像设备拍摄两种不同类型鸡的高光谱图像。使用顶点成分分析（VCA）算法提取鸡冠端元光谱，得到低产蛋鸡和正常鸡的鸡冠光谱特征曲线。接下来，采用快速连续小波变换（FCWT）对特征曲线的数据进行分析，以获得二维光谱特征图像数据集。最后，参照低产蛋鸡和正常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来测试模型的准确性时，我们发现准确率为 0.975%。这一结果表明，我们的识别方法结合了高光谱成像技术、FCWT 数据分析方法和 CNN 深度学习模型，在蛋鸡养殖场中非常高效和精确。此外，由于 FCWT 对数据信号分析和处理具有高效率和高分辨率的特点，尝试将其用于高光谱数据的分析和处理，将对高光谱技术在其他领域的研究和应用产生重大影响。
