本文選題：種子胚　切入點(diǎn)：超聲波檢測(cè)　出處：《聲學(xué)學(xué)報(bào)》2017年05期

【摘要】：提出了一種基于空氣耦合超聲波技術(shù)的玉米種子蟲(chóng)蛀孔洞顆粒和完好顆粒分類(lèi)識(shí)別方法·首先根據(jù)玉米顆粒的彈性模量、泊松比和密度等物理量計(jì)算出了玉米顆粒的聲速,并根據(jù)檢測(cè)精度需求設(shè)定了激勵(lì)信號(hào)頻率。然后采用MATLAB對(duì)采集的兩類(lèi)種子超聲波信號(hào)數(shù)據(jù)進(jìn)行分析處理,并分析了種子厚度和擺放方位對(duì)超聲波響應(yīng)特征的影響。最后建立了K近鄰(KNN)、簇類(lèi)獨(dú)立軟模式法(SIMCA)、Fisher線(xiàn)性判別(LDA)和決策樹(shù)(DT)識(shí)別模型,并對(duì)模型性能進(jìn)行了測(cè)試.結(jié)果表明;種子孔洞深度、胚部厚度和正反面方位不同,即超聲波在種子表面的反射程度不同、在種子中傳播聲程不同,則起聲波信號(hào)衰減程度不同,導(dǎo)致接收到信號(hào)的幅值不同,且樣本點(diǎn)在主成分分析(PCA)特征空間的分布也不同。4種識(shí)別模型均可以實(shí)現(xiàn)對(duì)兩類(lèi)玉米的分類(lèi)識(shí)別,其中KNN模型性能最佳,其對(duì)蟲(chóng)蛀孔洞顆粒和完好顆粒的正確識(shí)別率分別為98%100%,誤差帶為2%,0。此結(jié)果說(shuō)明采用空氣耦合超聲波技術(shù)可以實(shí)現(xiàn)對(duì)玉米種子蟲(chóng)蛀孔洞顆粒的檢測(cè)。
[Abstract]:In this paper, a classification and identification method of corn seed wormholes and intact particles based on air-coupled ultrasonic technique is proposed. Firstly, the sound velocity of corn grain is calculated according to the physical quantities such as elastic modulus, Poisson's ratio and density. The frequency of excitation signal is set according to the demand of detection precision, and then the two kinds of ultrasonic signal data of seed are analyzed and processed by MATLAB. The effects of seed thickness and placement azimuth on ultrasonic response were analyzed. Finally, the recognition models of K nearest neighbor KNNN, cluster independent soft pattern method SIMCAA Fisher linear discriminant and decision tree DTT were established, and the performance of the model was tested. The depth of the hole, the thickness of the embryo and the positive and negative directions are different, that is, the ultrasonic wave reflects on the surface of the seed with different degrees, and the sound path in the seed is different, the attenuation degree of the sound wave signal is different, and the amplitude of the received signal is different. The distribution of sample points in the feature space of PCA is also different. 4 kinds of recognition models can realize the classification recognition of two kinds of maize, and the KNN model has the best performance. The correct recognition rates of wormhole and intact particles are 98% and 2% respectively. The results show that the air coupled ultrasonic technique can be used to detect corn seed wormhole particles.
【作者單位】： 中國(guó)農(nóng)業(yè)大學(xué)信息與電氣工程學(xué)院;河南農(nóng)業(yè)大學(xué)機(jī)電工程學(xué)院;中國(guó)科學(xué)院聲學(xué)研究所;
【基金】：國(guó)家級(jí)星火計(jì)劃重點(diǎn)項(xiàng)目基金(2015GA600002) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專(zhuān)項(xiàng)基金(2016XD002)資助
【分類(lèi)號(hào)】：S435.132;TB559
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本文編號(hào)：1658965