基于本体与认知经验的农业机器人视觉分类决策方法

Visual Classification Decision-making Method for Agricultural Robots Based on Ontology and Cognitive Experience

基于小样本数据下认知经验知识辅助计算机进行决策,对实现农业领域机器人智能认知决策与助力智慧农业发展具有重要意义。本文在统计计数、支持向量机(SVM)等图像属性信息学习方法基础上,使用Protégé等工具,基于认知经验构建水果识别分类的专业知识库;然后根据图像颜色与形状信息,进行知识库搜索推理得到分类决策。实验在Fruit360数据集中共选择2091幅葡萄、香蕉、樱桃水果图像作为测试集,并各挑选30幅图像作为属性信息训练集与验证集,结果表明当前数据下葡萄与樱桃识别准确率为100%,香蕉识别准确率为93.30%。仅在知识库添加黄桃知识后,对984幅黄桃图像样本进行测试,其分类准确率为97.05%。表明本文方法能有效完成图像分类决策任务,且具有良好的过程可解释性、能力共享性和可拓展性。

It is of great significance to realize the intelligent cognitive decision-making ability of robots in the agricultural field and help the further development of smart agriculture that researchers use human cognitive experience and objective knowledge to assist computers and robots in object cognition and behavioral decision-making under the small sample data situation.On the prerequisites of the ability to recognize and judge basic attribute information such as image color and image shape by using methods such as statistical counting and support vector machine(SVM),tools such as Protégé was firstly used to build a professional knowledge base for fruit recognition and classification based on human cognitive experience and objective knowledge in object recognition.Then,under the rules set by artificial experience,the color information and shape information obtained from the image were used as the input of the knowledge base,and the classification results of the items in the image were obtained through matching reasoning.The experiments selected and used 2 091 images from the Fruit360 public data set for the first part experiment,which included multiple fruit images of grapes,bananas,and cherries.The research firstly selected 30 images of grapes,bananas and cherries as the training set and validation set for the computer's image attribute ability learning,and then the image classification performance was tested on the data set of the first part experiment.The experimental results showed that the image classification accuracy of grapes and cherries was 100%,and that of bananas was 93.30%.Subsequently,totally 984 yellow peach images in the Fruit360 public data set were selected as the data set for the second part experiment.By only adding the knowledge of yellow peach to the professional knowledge base built with ontology technology,the classification accuracy of the images can reach 97.05%.All experimental results showed that the proposed method can effectively accomplish the task of image classification decision-making and the method had good process interpretability,ability sharing and scalability.