CAJ | 학술논문

传统农田除草采用田间统一定量均匀喷洒,导致了除草剂浪费和环境污染问题. 智能变量喷施能够保护环境和提高作物产量,是促进农业可持续发展战略的重要途径. 为此,对经典的杂草监测参数进行改进并提出了正态分布下最小错误率的贝叶斯决策以实现精确变量喷施. 首先对农田图像进行灰度化、二值化及去噪等预处理;然后依据作物行中心线对农田图像进行网格单元的划分,并在网格单元格内提取改进的杂草监测参数;最后将贝叶斯决策分为两个阶段:线下阶段利用改进的杂草监测参数数据库计算正态分布参数,线上阶段根据改进的杂草监测参数实现正态分布下最小错误率的贝叶斯决策,从而为变量喷施提供决策依据. 实验结果表明:正态分布下最小错误率的贝叶斯决策正确率可达9 2%,与BP 算法和SVM 算法相比决策正确率相对较高.
전통농전제초채용전간통일정량균균분쇄,도치료제초제낭비화배경오염문제. 지능변량분시능구보호배경화제고작물산량,시촉진농업가지속발전전략적중요도경. 위차,대경전적잡초감측삼수진행개진병제출료정태분포하최소착오솔적패협사결책이실현정학변량분시. 수선대농전도상진행회도화、이치화급거조등예처리;연후의거작물행중심선대농전도상진행망격단원적화분,병재망격단원격내제취개진적잡초감측삼수;최후장패협사결책분위량개계단:선하계단이용개진적잡초감측삼수수거고계산정태분포삼수,선상계단근거개진적잡초감측삼수실현정태분포하최소착오솔적패협사결책,종이위변량분시제공결책의거. 실험결과표명:정태분포하최소착오솔적패협사결책정학솔가체9 2%,여BP 산법화SVM 산법상비결책정학솔상대교고.
Traditional farmland spraying is united quantitative and evenly, and this cause the waste of herbicide and envi-ronment pollution.Intelligent variables spraying, which not only can protect environment but also increase crop output, is the crucial way to promote sustainable agriculture development.In this paper, first modified the classic weed infestation rate( WIR) and then an accurate variables spraying based on the minimum error Bayes decision under normal distribution is presented.Firstly, farmland images are pre-processing using graying, binary and de-noising.Secondly, grid unit of farmland images are divided according to the centerline of crop rows and then compute the modified weed infestation rate ( MWIR) in the grid unit.Finally, bayesian decision is divided into two stages.Normal distribution parameters are com-puted base on database of MWIR in the offline stage, and Bayes online decision based on minimum error according to MWIR under normal distribution, which provide basis for decision making of intelligent variables spraying.The experi-mental results showed that the accuracy of this algorithm is as high as 92%, which exceeded BP algorithm and SVM algo-rithm.