加工番茄早疫病高光谱遥感识别研究  

Hyperspectral Remote Sensing Identification of Processing Tomato Early Blight Based on GA and SVM

作　　者：尹小君[1] 宁川[1] 张永才[1] 

机构地区：[1]石河子大学信息科学与技术学院,新疆石河子832000

出　　处：《遥感信息》2015年第2期94-98,共5页Remote Sensing Information

基　　金：中国科学院数字地球重点实验室开放基金项目(2012LDE011);国家科技支撑计划项目(2012BAH27B02);国家自然科学基金项目(31260291);石河子大学高层次人才基金项目(RCZX201226)

摘　　要：为了快速监测加工番茄早疫病发病率和加工番茄的产量和质量,防止病虫害的扩大,该文基于高光谱遥感数据和田间早疫病调查数据,以新疆天山北坡典型加工番茄种植区为研究区,分析加工番茄早疫病的病叶光谱响应特征,寻找早疫病的敏感波段,再利用遗传算法优化支持向量机的惩罚参数c和核函数参数g,对不同病害严重度的病叶进行识别。结果表明:不同病害严重度加工番茄早疫病病叶的敏感波段为628nm^643nm和689nm^692nm;遗传优化算法得出支持向量机最佳惩罚参数c为0.129,核函数参数g为3.479;分别利用多项式核、径向基核函数、Sigmoid核进行分类训练和测试,最佳分类模型为径向基核函数模型,训练准确率为84.615%,预测准确率为80.681%,高于默认参数c和g的支持向量机模型。说明通过遗传算法优化支持向量机的识别方法具有更高的精度,支持向量机为多波段协同识别病害严重度提供了新的思路。The yield and quality of processing tomato are seriously affected by early blight.Our study area is the main growing area of the north of Tianshan in Xinjiang.Based on the data of hyperspectral remote sensing and the data of survey in the field of early blight,we analyzed the spectral characterization in order to look for the sensitive wave bands and recognized the different disease severity with the genetic algorithm and support vector machine model.The result show that:①Sensitive bands of different disease severity levels of processing tomato early blight is 628nm^643nm and 689nm^692nm.②Using genetic algorithm optimize parameters of support vector machine,we get that the best penalty parameters is 0.129 and kernel function parameters is 3.479.③ We make classification training and testing by polynomial nuclear,radial basis function nuclear,and sigmoid nuclear,where the best classification model is the radial basis function nuclear of SVM.Training accuracy is 84.615%and testing accuracy is 80.681%.Those are higher than SVM with default parameters.So the method of support vector machine optimized by genetic algorithm has higher accuracy and support vector machine are offered a new idea of combined band to identify disease severity.

关 键 词：遗传算法 支持向量机 加工番茄 早疫病 病虫害识别 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]