基于偏好免疫网络和SVM算法的油茶果多特征识别  

Recognition of Camellia multi-features based on preference artificial immune network and support vector machine

作　　者：李昕 陈泽君[1] 李立君[2] 谭季秋[3] 吴发展 Li Xin;Chen Zejun;Li Lijun;Tan Jiqiu;Wu Fazhan(Hunan Academy of Forestry,Changsha 410004,China;School of Mechanical and Electrical Engineer,Center South University of Forestry and Technology,Changsha 410004,China;School of Mechanical Engineer,Hunan Institute of Engineering,Xiangtan 411104,China;Fengke Forestry Equipment Technology Co.,Ltd.,Zhuzhou 412000,China)

机构地区：[1]湖南省林业科学院,长沙410004 [2]中南林业科技大学机电工程学院,长沙410004 [3]湖南工程学院机械工程学院,湘潭411104 [4]株洲丰科林业装备科技有限责任公司,株洲412000

出　　处：《农业工程学报》2020年第22期205-213,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划项目(2016YFD0702100);湖南省重点研发计划(2016NK2142);湖南省重点研发计划项目(2018NK2065)。

摘　　要：针对油茶果采摘脱壳后存在的果壳籽粒分选效率较低的问题,该研究提出了一种结合人工免疫网络(aiNet)与支持向量机(Support Vector Machine)的多特征智能分选算法。该方法利用了免疫算法的多特征聚类特点与支持向量机的二分性特点,对油茶果壳与籽粒的延伸率、圆形度、圆满度、色差分量等6个特征进行分选。试验结果表明,该研究提出的方法在分选识别率上达到了97.4%,时间平均值为600 ms,证明了这种方法在油茶果壳籽粒分选作业中的实时性与有效性。通过与其他智能分选算法的效率对比分析证明,该研究提出的方法在效率上更优,更加适合油茶脱壳生产线的实时性要求。Automation processing has become particularly important for the Camellia oleifera industry in Southern China,as the agricultural economy is ever increasing.Fruit shelling of Camellia oleifera is a very critical link in the production line.There are still some problems so far in the sorting and recognition system for the Camellia oleifera sheller,such as single-feature recognition method,great disturbance by target color,and relatively low adaptive function.This study aims to propose a multi-features intelligent sorting algorithm,combining the artificial immune network(aiNet)and Support Vector Machine(SVM),in order to fully utilize the multi-feature clustering feature of immune algorithm,and the dichotomy feature of SVM algorithm.Six morphological and color characteristics of shell kernel in a Camellia oleifera were extracted,including elongation,roundness,completeness,R component,G component,and B component of color feature.These characteristics were used to sort and identify the shell and kernel of Camellia oleifera.The collected images were first preprocessed,then three morphological features were integrated into the aiNet algorithm for multi-features comprehensive identification,finally three-color features were input into the SVM algorithm for the recognition of color features.Since the color of fruit shells and seeds varied in different storage periods,3 and 12 days were selected to obtain the obvious color characteristics of Camellia oleifera fruits,considering the influence of temperature,and humidity,on the picking Camellia oleifera fruits.In the experimental test,the multi-features immune network combined with SVM algorithm significantly reduced the complexity of multi-dimensional operation while saved the operation time.The results showed that the sorting efficiency of Camellia oleifera fruit reached 97.4%in 3 days,and 76.6%in 12 days,indicating a high separation efficiency.The recognition time reached an average of 600 ms and a minimum of 510 ms,where the recognition time was the sum of the consumption

关 键 词：图像处理 图像识别 油茶果 分选 多特征 免疫算法 SVM 

分 类 号：S225.93[农业科学—农业机械化工程]