雷雨,韩德俊,曾庆东,何东健.基于高光谱成像技术的小麦条锈病病害程度分级方法[J].农业机械学报,2018,49(5):226-232.
LEI Yu,HAN Dejun,ZENG Qingdong,HE Dongjian.Grading Method of Disease Severity of Wheat Stripe Rust Based on Hyperspectral Imaging Technology[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):226-232.
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基于高光谱成像技术的小麦条锈病病害程度分级方法   [下载全文]
Grading Method of Disease Severity of Wheat Stripe Rust Based on Hyperspectral Imaging Technology   [Download Pdf][in English]
投稿时间:2017-11-08  
DOI:10.6041/j.issn.1000-1298.2018.05.026
中文关键词:  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
基金项目:陕西省重点产业链项目(2015KTZDNY01-06)
作者单位
雷雨 西北农林科技大学 
韩德俊 西北农林科技大学 
曾庆东 西北农林科技大学 
何东健 西北农林科技大学 
中文摘要: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HyperSIS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555Nm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。
LEI Yu  HAN Dejun  ZENG Qingdong  HE Dongjian
Northwest A&F University,Northwest A&F University,Northwest A&F University and Northwest A&F University
Key Words:wheat stripe rust  disease severity  hyperspectral imaging  principal component analysis  grading  Otsu method
Abstract: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 HyperSIS hyperspectral system covering the visible and near-infrared region (400~1000Nm). Secondly, via the analysis of spectral reflectance of leaf and background regions, there were obvious differences in spectral reflectance at the 555Nm wavelength. Therefore, the image of the 555Nm wavelength was named the feature image, which was manipulated by threshold segmentation to obtain a mask image. The logical and operation was conducted 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 leaf. To verify the effectiveness of the proposed method, a total of 270 leaf samples were collected for the performance evaluation. Experimental results showed that 265 samples could be accurately classified at different disease severities of wheat stripe rust and the overall classification accuracy was 9815%. In conclusion, the experimental results indicated that the method using hyperspectral imaging technology proposed is able to satisfy the precision demand of quantitative calculation and provide a foundation for evaluating the field disease level of wheat stripe rust and a new idea for resistance identification method of wheat stripe rust.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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