一种基于偏好免疫网络多特征辨识的油茶果分选识别方法

针对油茶果采摘、脱壳后机器视觉分选效率不高的问题,提出一种多特征偏好人工免疫网络算法,该算法应用人工免疫网络的多目标优化与偏好数据库特征,提取油茶目标的颜色、形态多特征输入免疫网络进行仿真测试。试验结果表明,本文提出的多特征偏好免疫网络的识别率最高达到90%以上。相比单特征分选方法有了较大的提升,证明本文分选方法的有效性,并为农林业目标智能化分选辨识提供一种可行的方案。

A method of Camellia fruit sorting based on multi features identification in preference aiNet

In order to solve the problem of low sorting efficiency after picking and shelling of Camellia fruits in machine vision, the paper proposes a preference artificial immune network algorithm (aiNet) with multi features, which applies the multi objective optimization of artificial immune network and preference database to extract the multi features in color and shape of Camellia objects to input into the immune network for simulation test. The test results show that the multi features preference immune network proposed in this paper is feasible. The recognition rate of the network has reached 90%, and the minimum identification time is 60 ms. Compared with the single feature sorting method, this method is more effective, which provides a feasible scheme for the intelligent sorting method of agricultural and forestry targets.