期刊论文

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

Chen, Z.

中文

农业工程学报

1002-6819

2020

36

22

205-213

10.11975/j.issn.1002-6819.2020.22.023

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)。

本校为第一机构

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, combini...