熊俊涛,廖世盛,梁俊浩,韦婷婷,陈淑绵,郑镇辉.基于本体与认知经验的农业机器人视觉分类决策方法[J].农业机械学报,2023,54(2):208-215.
XIONG Juntao,LIAO Shisheng,LIANG Junhao,EI Tingting,CHEN Shumian,ZHENG Zhenhui.Visual Classification Decision-making Method for Agricultural Robots Based on Ontology and Cognitive Experience[J].Transactions of the Chinese Society for Agricultural Machinery,2023,54(2):208-215.
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基于本体与认知经验的农业机器人视觉分类决策方法   [下载全文]
Visual Classification Decision-making Method for Agricultural Robots Based on Ontology and Cognitive Experience   [Download Pdf][in English]
投稿时间:2022-04-08  
DOI:10.6041/j.issn.1000-1298.2023.02.020
中文关键词:  智慧农业  认知决策  图像分类  属性学习  本体技术  知识库
基金项目:国家自然科学基金项目(32071912)、广东省农业科技创新十大主攻方向“揭榜挂帅”项目(2022SDZG03)和广东省大学生科技创新培育专项资金项目(pdjh2023a0075)
作者单位
熊俊涛 华南农业大学 
廖世盛 华南农业大学 
梁俊浩 华南农业大学 
韦婷婷 华南农业大学 
陈淑绵 华南农业大学 
郑镇辉 华南农业大学 
中文摘要: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。
XIONG Juntao  LIAO Shisheng  LIANG Junhao  EI Tingting  CHEN Shumian  ZHENG Zhenhui
South China Agricultural University
Key Words:smart agriculture  cognitive decision-making  image classification  attribute learning  ontology technology  knowledge base
Abstract: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 2091 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.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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