本文關(guān)鍵詞：舍飼環(huán)境下母羊產(chǎn)前典型行為識別方法研究　出處：《內(nèi)蒙古農(nóng)業(yè)大學(xué)》2017年博士論文　論文類型：學(xué)位論文

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【摘要】：肉羊產(chǎn)業(yè)作為內(nèi)蒙古地區(qū)的特色產(chǎn)業(yè),對內(nèi)蒙古的經(jīng)濟(jì)發(fā)展起到至關(guān)重要的作用。近年來,為恢復(fù)草原生態(tài)實(shí)現(xiàn)可持續(xù)發(fā)展,實(shí)施了輪牧禁牧政策,肉羊的飼養(yǎng)模式由傳統(tǒng)的放養(yǎng)逐漸轉(zhuǎn)變?yōu)樵O(shè)施養(yǎng)殖,規(guī)�；�、集約化養(yǎng)殖成為未來肉羊產(chǎn)業(yè)的主要發(fā)展趨勢。隨著舍飼飼養(yǎng)羊只密度的增加,養(yǎng)殖環(huán)境無法得到保障,導(dǎo)致羊只處于亞健康或不健康狀態(tài),羊的發(fā)病率大幅度上升;而孕期母羊的健康狀況又會直接影響仔羔的生存狀態(tài),同時(shí)與飼養(yǎng)者的經(jīng)濟(jì)效益密切相關(guān),因此利用自動(dòng)化、智能化方法實(shí)時(shí)、準(zhǔn)確的監(jiān)測母羊產(chǎn)前行為信息,為提高母羊的生產(chǎn)力和繁殖效率及母羊和羔羊的健康水平具有十分重要的意義。本文以小尾寒羊母羊?yàn)檠芯繉ο?獲取了母羊產(chǎn)前站立、行走、趴臥和刨地四種典型運(yùn)動(dòng)行為及采食和飲水行為信息,并對母羊產(chǎn)前運(yùn)動(dòng)行為信息進(jìn)行了小波閾值去噪、特征參數(shù)的提取以及行為分類識別模型的建立。取得了如下結(jié)論:(1)設(shè)計(jì)了可穿戴式的、以三軸加速度傳感器MPU6050為核心的母羊產(chǎn)前運(yùn)動(dòng)行為實(shí)時(shí)監(jiān)測裝置,以及以紅外傳感器為核心的母羊產(chǎn)前采食及飲水行為監(jiān)測裝置,實(shí)現(xiàn)了對母羊產(chǎn)前運(yùn)動(dòng)、采食及飲水行為信息的連續(xù)、實(shí)時(shí)監(jiān)測。采用LabVIEW軟件設(shè)計(jì)了上位機(jī)軟件,實(shí)現(xiàn)母羊產(chǎn)前典型行為信息的采集、顯示與存儲。試驗(yàn)證明了裝置的準(zhǔn)確性、可靠性和有效性。(2)利用小波閾值去噪方法對母羊產(chǎn)前四種運(yùn)動(dòng)行為三軸加速度數(shù)據(jù)進(jìn)行去噪,并提出改進(jìn)的小波閾值函數(shù)去噪方法。通過MATLAB軟件平臺實(shí)現(xiàn)運(yùn)動(dòng)行為加速度信號的去噪處理。試驗(yàn)表明,改進(jìn)后的小波閾值函數(shù)去噪算法能夠取得較好的去噪效果。(3)對去噪后的母羊產(chǎn)前運(yùn)動(dòng)行為加速度數(shù)據(jù)進(jìn)行特征提取,選取了方差、主峰頻率及頻率能量多個(gè)表征母羊行為的加速度數(shù)據(jù)特征,采用主元分析方法對特征值進(jìn)行降維。試驗(yàn)表明,經(jīng)過特征降維之后的母羊行為識別率提高了 9.8%。(4)針對母羊運(yùn)動(dòng)行為中趴臥與站立行為分類識別率低的問題,提出在MATLAB軟件平臺上,采用K-means聚類算法對母羊產(chǎn)前趴臥行為進(jìn)行識別,經(jīng)驗(yàn)證此算法對趴臥行為的識別率能夠達(dá)到99%;利用BP神經(jīng)網(wǎng)絡(luò)算法對剩余的三種運(yùn)動(dòng)行為:站立、行走、刨地行為進(jìn)行識別。試驗(yàn)結(jié)果表明,神經(jīng)網(wǎng)絡(luò)分類算法對母羊產(chǎn)前運(yùn)動(dòng)行為平均識別率達(dá)到78.93%;針對神經(jīng)網(wǎng)絡(luò)識別算法對母羊產(chǎn)前非標(biāo)準(zhǔn)運(yùn)動(dòng)行為如站立時(shí)蹭欄桿、撓耳朵等行為識別效果差的問題,提出了采用遞階遺傳算法對BP神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)和參數(shù)進(jìn)行改進(jìn)。經(jīng)驗(yàn)證,改進(jìn)后的識別算法泛化能力有了明顯的改善,對母羊產(chǎn)前運(yùn)動(dòng)行為平均識別率達(dá)到了 89.12%。(5)針對母羊產(chǎn)前采食及飲水行為監(jiān)測結(jié)果中存在的誤差,提出了通過母羊的運(yùn)動(dòng)行為狀態(tài)對母羊產(chǎn)前采食及飲水行為進(jìn)行校正。經(jīng)驗(yàn)證,校正后的母羊產(chǎn)前采食及飲水行為識別率提高了 16%,完全能夠滿足母羊行為采食及飲水行為識別的要求。(6)本文對母羊不同分娩時(shí)期運(yùn)動(dòng)行為、采食及飲水行為分配的時(shí)間及行為節(jié)律進(jìn)行了統(tǒng)計(jì)分析,所得行為規(guī)律與國內(nèi)外其他研究學(xué)者的結(jié)論相一致,進(jìn)一步證明了所設(shè)計(jì)的母羊產(chǎn)前行為識別算法模型的準(zhǔn)確性及有效性。
[Abstract]:The sheep industry is a pillar industry of Inner Mongolia area, play an important role in the economic development of Inner Mongolia. In recent years, for the restoration of grassland ecological sustainable development, the implementation of the grazing sheep grazing policy, feeding mode has gradually changed from the traditional stocking for breeding facilities, large-scale, intensive farming has become a main trend for the development of the sheep industry with the increase of sheep feeding breeding density, breeding environment can not be guaranteed, resulting in sheep in sub healthy or unhealthy, the incidence rate of sheep increased significantly; and the health status of pregnant ewes and lambs will directly affect the offspring's survival condition, at the same time with the breeders is closely related to the economic benefit, therefore the use of automation, intelligent method for real-time monitoring, accurate prenatal ewe behavior information, to improve the health level of ewe productivity and reproductive efficiency and the ewe and lamb There is very important significance. In this paper, small tail Han sheep as the research object, the ewe prenatal standing, walking, lying and digging, four kinds of typical behavior and feeding and drinking behavior information, and the information of the ewe prenatal exercise behavior of wavelet threshold denoising, feature parameter extraction and behavior classification model. The results are as follows: (1) the wearable design, ewe prenatal exercise real-time monitoring device behavior with three axis acceleration sensor MPU6050 as the core, and the infrared sensor as the core of the ewe prenatal feeding and drinking behavior monitoring device, the realization of ewes feeding and prenatal exercise. Drinking behavior information for continuous, real-time monitoring. The upper computer software is designed by LabVIEW software, realize the information collection of prenatal behaviors typical of ewes, display and storage. The test indicates that the accuracy of the device, reliability And effective. (2) using wavelet threshold denoising method of four kinds of prenatal exercise behavior ewe three axis acceleration data denoising, and puts forward the improved wavelet threshold function denoising method. Achieve the denoising motion acceleration signal through the MATLAB software platform. The results show that the improved wavelet threshold function denoising the algorithm can obtain better denoising results. (3) of the ewes prenatal exercise acceleration data after denoising behavior for feature extraction, selection variance, peak frequency and frequency characteristic of acceleration data of multiple energy Behavior Characterization of ewes, using principal component analysis method of characteristic values of dimensionality reduction. The results show that after ewe behavior after recognition feature reduction rate increased by 9.8%. (4) according to the classification of lying and standing behavior in the low rate of ewe behavior problems, put forward on the MATLAB software platform, using K-means clustering algorithm Method of prenatal ewe lying behavior recognition. The experiment prove that the algorithm for recognition of lying behavior rate can reach 99%; three kinds of residual motion behavior using BP neural network algorithm: standing, walking, digging behavior recognition. The experimental results show that the neural network classification algorithm average recognition on ewe prenatal exercise behavior reached 78.93%; the neural network recognition algorithm for non standard behavior such as prenatal ewe standing against the railing, the effect of recognition of the problem of poor behavior such as scratching his ear, put forward the hierarchical genetic algorithm structure and parameters of the BP neural network was improved. After verification, the improved generalization ability of recognition algorithm is significantly improved. The average recognition rate of ewe prenatal exercise behavior reached 89.12%. (5) according to the errors of ewes feeding and drinking behavior of prenatal monitoring results, put forward dynamic behavior through ewe transport State Food and drinking behavior correction to prenatal ewes. After validation, the correction rate increased by 16% ewes after prenatal feeding and drinking behavior recognition, can fully meet the behavior of ewes feeding and drinking behavior recognition requirements. (6) according to different delivery period of ewe exercise behavior, and behavior and foraging time rhythm the distribution of drinking behavior was analyzed, the behavior is consistent with other domestic and foreign scholars' research conclusion, further proves the accuracy and effectiveness of ewe prenatal behavior identification algorithm design model.

【學(xué)位授予單位】：內(nèi)蒙古農(nóng)業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TP274;S826

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本文編號：1398096