基于高光谱成像技术的小麦条锈病病害程度分级方法  

Grading Method of Disease Severity of Wheat Stripe Rust Based on Hyperspectral Imaging Technology

作　　者：雷雨[1,2] 韩德俊[3,4] 曾庆东[4] 何东健 LEI Yu1,2,HAN Dejun3,4,ZENG Qingdong4,HE Dongjian1,5(1. College of Mechanical and Electronic Engineering, Northwest A＆F University, Yangling, Shaanxi 712100, China;2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Northwest A＆F University, Yangling , Shaanxi 712100, China;3. College of Agronomy, Northwest A＆F University, Yangling , Shaanxi 712100, China;4. State Key Laboratory of Crop Stress Biology for Arid Areas, Northwest A＆F University, Yangling , Shaanxi 712100, China;5. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, Chin)

机构地区：[1]西北农林科技大学机械与电子工程学院,陕西杨凌712100 [2]西北农林科技大学农业部农业物联网重点实验室,陕西杨凌712100 [3]西北农林科技大学农学院,陕西杨凌712100 [4]西北农林科技大学旱区作物逆境生物学国家重点实验室,陕西杨凌712100 [5]陕西省农业信息感知与智能服务重点实验室,陕西杨凌712100

出　　处：《农业机械学报》2018年第5期226-232,共7页Transactions of the Chinese Society for Agricultural Machinery

基　　金：陕西省重点产业链项目(2015KTZDNY01-06)

摘　　要：为了快速、准确地对小麦条锈病病害程度进行分级评估,提出了一种基于高光谱成像技术的小麦条锈病病害程度分级方法。首先利用Hyper SIS高光谱成像系统采集受条锈菌侵染后不同发病程度的小麦叶片高光谱图像,通过分析叶片区域与背景的光谱特征,对555 nm波长的特征图像进行阈值分割获得掩膜图像,并用掩膜图像对高光谱图像进行掩膜处理,提取仅含叶片的高光谱图像;然后用主成分分析法(Principal component analysis,PCA)得到利于条锈病病斑和健康区域分割的第2主成分(The second principal component,PC2)图像,采用最大类间方差法(Otsu)分割出条锈病病斑区域;最后根据条锈病病斑区域面积占叶片面积的比例对小麦条锈病病害程度进行分级。试验结果表明:测试的270个不同小麦条锈病病害等级的叶片样本中,265个样本可被正确分级,分级正确率为98.15%。该研究为田间小麦条锈病害程度评估提供了基础,也为小麦条锈病抗性鉴定方法提供了新思路。Wheat stripe rust caused by Puccinia striiformis f. sp. tritici,is one of the most important and devastating diseases in wheat production. Identification and classification of wheat stripe rust plays an important role in high-quality production of wheat,which helps to quantitatively assess the level of wheat stripe rust severity in the field to make strategies to achieve effective control for wheat stripe rust in early.Currently,estimation disease severity of wheat stripe rust is mainly relied on naked-eye observation according to the manual field investigation. However,this method is labour-intensive,time-consuming,besides requiring workers with high professional knowledge. In order to quickly and accurately evaluate the disease level of wheat stripe rust,a novel grading method of disease severity of wheat stripe rust based on hyperspectral imaging technology was proposed. Firstly,hyperspectral images of 320 infected at different levels and 40 healthy wheat leaf samples were captured by a Hyper SIS hyperspectral system covering the visible and near-infrared region（ 400 ～ 1 000 nm）. Secondly,via the analysis of spectral reflectance of leaf and background regions,there were obvious differences in spectral reflectance at the555 nm wavelength. Therefore,the image of the 555 nm wavelength was named the feature image,which was manipulated by threshold segmentation to obtain a mask image. The logical and operation wasconducted by using the original hyperspectral image and mask image to remove the background information. Thirdly,the principal component analysis（ PCA） method was used for the dimension reduction of hyperspectral images. The operation results showed that the second principal component image（ PC2） can significantly identify the stripe rust spot area and healthy area. On this basis,stripe rust spots area was efficiently segmented by using an Otsu method. Finally,the degree of the disease severity of wheat stripe rust was graded according to the proportion of stripe rust spots area on a whole le

关 键 词：小麦条锈病 病害程度 高光谱成像 主成分分析 分级 最大类间方差法 

分 类 号：S435.121.42[农业科学—农业昆虫与害虫防治] TP312[农业科学—植物保护]