Identification of damaged corn seeds using air-coupled ultrasound  

作　　者：Jin Yanyun Gao Wanlin Zhang Han An Dong Guo Sihan Saeed Iftikhar Ahmed Liu Yunling 

机构地区：[1]College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China [2]Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190,China

出　　处：《International Journal of Agricultural and Biological Engineering》2016年第1期63-70,共8页国际农业与生物工程学报（英文）

基　　金：This work was supported by National Key Technology R&D Program of China during the 12th Five-Year Plan(Grant#:2012BAD35B02).

摘　　要：Corn,an important staple in many countries around the world,is subject to a very inefficient germination rate due to worm-damaged seeds.However,air-coupled ultrasound is a rapid,safe and widely accepted method for the early detection of such damage.In this study,the current effectiveness and future prospects of this technique for identifying damaged seeds were explored.The presented procedure started with drawing a sample of 810 seed particles,consisting of 400 that were intact,400 manually damaged and 10 damaged by worms.Then the principal component analysis(PCA)method was used to reduce the dimensions of air-coupling ultrasonic information and extract the top ten principal components.Finally,a KNN decision tree by using SIMCA software and a Fisher recognition model by using MATLAB software were constructed.The pattern recognition was established by using KNN,which has the most accurate recognition rate.The correct recognition rate of modeling for the front and back data of the intact particles was 98%and 100%,respectively;and for the manually damaged particles,99%and 97%,respectively.The results show that the model developed by using air-coupled ultrasonic data can classify corn seed particles both with and without holes to provide a basis for the development of a seed selection system,which has a significant role in improving the clarity and the germination rate.

关 键 词：damaged corn seed identification air-coupled ultrasonic principal component analysis KNN 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]