基于物联网的远程温室视觉监控系统设计与实现

在当前智慧农业的大环境下,农作物生长过程的识别与监控问题一直是一项具有挑战性的任务,基于此提出一种基于物联网的远程温室视觉监控系统,系统通过LoRa无线通信技术监测温室内的温湿度、光照强度等环境参数,能够及时监测到农作物的生长状况,并实现自动通风、自动补光等功能。在PC端的Qt上位机实时监测温室内的环境信息并控制环境参数,通过OV9726摄像头对农作物进行监测,所获得的生长状态信息传输到S3C6410集中控制模块进行处理,结合克隆选择算法和朴素贝叶斯分类器对叶片进行识别处理。本系统采用LoRa模块进行自组网来实现环境监测,将Linux操作系统移植到集中控制模块,为视觉系统软硬件平台的搭建做准备工作,所使用的组合算法能够使得农作物叶片识别率达到95.3%,识别时间达到8.4 ms,对于叶片识别精度等方面有着明显的提升,经过实验充分验证本系统所使用的设备与算法的有效性。     

Constructing naive Bayesian classifiers for veterinary medicine: A case study in the clinical diagnosis of classical swine fever

For diseases of which the clinical diagnosis is uncertain, naive Bayesian classifiers can be of assistance to the veterinary practitioner. These simple probabilistic models have proven to be very powerful for solving classification problems in a variety of domains, but are not yet widely applied within the veterinary domain. In this paper, naive Bayesian classifiers and methods for their construction are reviewed. We demonstrate how to construct full and selective classifiers from a data set and how to build such classifiers from information in the literature. As a case study, naive Bayesian classifiers to discriminate between classical swine fever (CSF)-infected and non-infected pig herds were constructed from data collected during the 1997/1998 CSF epidemic in the Netherlands. The resulting classifiers were studied in terms of their accuracy and compared with the optimally efficient diagnostic rule that was reported earlier by Elbers et al. (2002). The classifiers were found to have accuracies within the range of 67-70% and performed comparable to or even better than the diagnostic rule on the available data. In contrast with the diagnostic rule, the classifiers had the advantage of taking both the presence and the absence of particular clinical signs into account, which resulted in more discriminative power. These results indicate that naive Bayesian classifiers are promising tools for solving diagnostic problems in the veterinary field.     

基于朴素贝叶斯的渔业文本分类器研究

通过阐述朴素贝叶斯文本分类器的算法原理及其用于建立渔业文本分类器的优点,给出了基于朴素贝叶斯的渔业文本分类器的基本结构,并用实验验证了该结构的性能。结果表明,基于渔业词库的朴素贝叶斯渔业文本分类器具有比普通文本分类器更好的性能。     

基于Naive Bayes算法的大豆病害诊断研究

介绍了Naive Bayes算法的基本理论。以UCI数据库中的大豆数据集为实例,研究了Naive Bayes算法在大豆病害诊断中的应用。试验结果表明,Naive Bayes算法的预测精度优于决策树C4.5算法和最近邻INN算法。     

基于人工智能的小麦高效育种信息交互系统构建

小麦是人类社会重要的粮食资源之一,因此基于人工智能技术构建高效育种信息交互平台对于高质高产的小麦种植具有重要的战略价值。高效育种信息交互平台的搭建关键在于核心数据的准确识别与分类,基于此该研究提出了一种Naive Bayes（朴素贝叶斯）-AdaBoost策略,应用于小麦育种信息数据的分类与识别,并实现构筑交互平台。在该策略中AdaBoost主要用于对Naive Bayes的弱分类器进行迭代,形成强分类器,同时过滤并优化核心词汇,达到提高分类识别准确度的目的。结果显示,与传统Naive Bayes方法相比该方法准确率提高了12.2个百分点,识别的准确率达到99.2%,而此时基于Naive Bayes、决策树、支持向量机3种方法的准确率分别为87.0%、86.6%和85.6%。结果表明,该研究所提方法在面对复杂数据分类识别的场景中具有较大的应用潜力。     

基于朴素贝叶斯算法的语言特征描述研究

近年来计算语言学理论与方法的迅速发展与逐渐成熟带动了文本挖掘等交叉领域的进步,取得了一定的实践成果,但通过交叉领域的进步反过来促进语言学发展的研究却相对较少。本研究从网络新闻语料入手,基于朴素贝叶斯算法,探究了特征词数与真实语料的关系,为理论与应用研究提供了相关数据与思路。     

一种增量式贝叶斯分类的算法

自动分类是数据挖掘和机器学习中非常重要的研究领域。针对难以获得大量有类标签的训练集问题,提出了基于小规模训练集的增量式贝叶斯Bayes分类,给出增量式Bayes分类机理参数计算及其算法。对算法分两种情况处理,第一种情况是新增样本有类别标签,利用现有分类器检验其类标签,如果匹配则保留当前分类器,否则利用新样本修正分类器;第二种情况是新增样本无类别标签,则利用现有分类器为其训练类标签,然后利用新样本来修正分类器。试验结果表明,该算法是可行有效的,比Naive Bayes分类算法有更高的精度。增量式Bayes分类算法的提出为分类器的更新提供了一条新途径。