| Relief-F筛选波段的小麦白粉病早期诊断研究 |
| |
| 引用本文： | 黄林生,张庆,张东彦,林芬芳,徐超,赵晋陵.Relief-F筛选波段的小麦白粉病早期诊断研究[J].红外与激光工程,2018,47(5):523001-0523001(8). |
| |
| 作者姓名： | 黄林生 张庆 张东彦 林芬芳 徐超 赵晋陵 |
| |
| 作者单位： | 1.安徽大学 安徽省农业生态大数据工程实验室,安徽 合肥 230601; |
| |
| 基金项目： | 安徽省科技重大专项（16030701091）;国家自然科学基金（41771463，41771469，41301471）;安徽省自然科学基金（1608085MF139） |
| |
| 摘 要： | 为了准确监测小麦白粉病染病早期病情，给喷药防治提供技术指导，论文将染病初期的小麦叶片作为研究对象。首先，利用高光谱图像数据，通过图像特征分割出叶片区域和病斑区域，定量计算病情严重度；其次引入Relief-F算法提取染病早期最敏感波段和波段差，计算出白粉病病害指数PMDI （Powdery mildew disease index）；并通过分析病情指数DI （Disease index）与11种植被指数（含PMDI指数）的相关性及线性模型，得出PMDI模型有最高的决定系数（R2=0.839 9）和最低的均方根误差（RMSE=4.522 0），效果优于其他病害植被指数的结果（其中，Normalized Difference Vegetation Index，NDVI的模型决定系数最高，R2=0.777 1，RMSE=5.336 4）；最后，选择PMDI和NDVI植被指数分别构建小麦白粉病染病早期病情严重度的支持向量回归模型。结果表明:经敏感波段筛选构建的PMDI指数的预测结果更好，预测模型的R2=0.886 3，RMSE=3.553 2，可以实现小麦白粉病早期无损诊断，这为指导作物病害喷药防治提供重要的技术支撑。
|
| 关 键 词： | 图像分割 光谱特征 Relief-F 支持向量回归 小麦白粉病 |
| 收稿时间： | 2017-11-05 |
Early diagnosis of wheat powdery mildew based on Relief-F band screening |
| |
| Affiliation: | 1.Anhui Engineering Laboratory of Agro-Ecological Big Data,Anhui University,Hefei 230601,China;2.School of Geography and Remote Sensing,Nanjing University of Information Science & Technology,Nanjing 210044,China |
| |
| Abstract: | In order to inspect accurately the condition of early wheat powdery mildew, and also to provide technical support for spraying pesticides, in this study hyperspectral imagery data of different disease severity for wheat leaves were collected at the early infection stage. Firstly, the leaf area and lesion area were segmented by image features, and then the disease severity was calculated quantitatively. Secondly, the Relief-F algorithm was introduced to select the most sensitive band and band difference, on the basis, the powdery mildew disease index(PMDI) was calculated. Moreover, the correlations between disease index(DI) and 11 vegetation indices(Including PMDI index) were analyzed, it was found that the PMDI index has the highest coefficient of determination(R2=0.839 9) and the lowest root-mean-square error(RMSE) which is 4.522 0. It was better than that of other disease vegetation indices, in which the result of normalized difference vegetation index(NDVI) was the highest, the determination coefficient is 0.777 1 and the RMSE equals 5.336 4. Finally, the support vector regression(SVR) models of PMDI and NDVI indexes were established, respectively, to further compare the retrieval performance for disease severity of early wheat powdery mildew. The result shows that the prediction model of PMDI index is better than NDVI index, the R2 is 0.886 3 with RMSE=3.553 2. It can be concluded that the developed method can effectively realize nondestructive diagnosis of early wheat powdery mildew, and provide important help for the spraying and disease control. |
| |
| Keywords: | |
|
| 点击此处可从《红外与激光工程》浏览原始摘要信息 |
|
点击此处可从《红外与激光工程》下载全文 |
|
