Banakar Ahmad, Sadeghi Mohammad, Shushtari Abdolhamid
Department of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Poultry Disease, Razi Vaccine and Serum Research Institute, Karaj, Iran.
Comput Electron Agric. 2016 Sep;127:744-753. doi: 10.1016/j.compag.2016.08.006. Epub 2016 Aug 13.
In commercial poultry production there are a number of diseases which are of particular importance due to the heavy economic losses that can arise if a flock becomes infected. The development of an automated and rapid disease detection system would therefore be of considerable benefit to both production and animal welfare. This study represents an intelligence device for diagnosing avian diseases by using Data-mining methods and Dempster-Shafer evidence theory (D-S). 14-day-old chickens were divided into four groups. Each group was deliberately infected with a disease: Newcastle Disease (ND), Bronchitis Virus (BV), Avian Influenenza (AI), and the last group was considered as control samples. Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) were used to process the chicken's sound signals in frequency and time-frequency domains, respectively. In order to achieve information, 25 statistical features from frequency domains, and 75 statistical features from time-frequency domains were extracted. During dimensionality reduction stage, the best features of the sound signals were selected, using improved distance evaluation (IDE) method. The chicken's sound signals were analyzed in two consecutive days after virus infection. Support vector machine (SVM) was used as the classifier in this study. The first classification was done with SVM and based on sound features in frequency and time-frequency domains with accuracy of 41.35 and 83.33%, respectively. The accuracy of the method based on D-S infusion of sound data reached 91.15%. The developed model based on achievement result could diagnose Newcastle Disease, Bronchitis Virus and Avian Influenza from sound signals.
在商业家禽养殖中，有许多疾病因一旦鸡群感染就可能造成巨大经济损失而格外重要。因此，开发一种自动化快速疾病检测系统对生产和动物福利都将大有裨益。本研究提出一种利用数据挖掘方法和Dempster-Shafer证据理论（D-S）诊断禽类疾病的智能设备。将14日龄的鸡分为四组。每组故意感染一种疾病：新城疫（ND）、支气管炎病毒（BV）、禽流感（AI），最后一组作为对照样本。分别使用快速傅里叶变换（FFT）和离散小波变换（DWT）在频域和时频域处理鸡的声音信号。为获取信息，从频域提取了25个统计特征，从时频域提取了75个统计特征。在降维阶段，使用改进距离评估（IDE）方法选择声音信号的最佳特征。在病毒感染后的连续两天对鸡的声音信号进行分析。本研究使用支持向量机（SVM）作为分类器。首次分类是用SVM基于频域和时频域的声音特征进行的，准确率分别为41.35%和83.33%。基于声音数据的D-S融合方法的准确率达到91.15%。基于所得结果开发的模型能够从声音信号中诊断新城疫、支气管炎病毒和禽流感。
