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Discrimination of yellow rust and powdery mildew in wheat at leaf level using spectral signatures

机译:利用光谱特征鉴别小麦叶片上的黄锈和白粉病

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Yellow rust (Puccinia striiformis f. sp. Tritici) and powdery mildew (Blumeria graminis) are two serious diseases that severely intimidated yield and grain quality of winter wheat around the world. Since the preferable habitat conditions of them are similar, there is a high possibility that both diseases occurred in field simultaneously. To facilitate a differentiation of control procedures (i.e. using different fungicide), the discrimination of yellow rust and powdery mildew is a necessity. As a fast and nondestructive technique in obtaining the plant status information in real time, remote sensing has been successfully applied in the monitoring of crop diseases in several cases. However, studies addressing the discrimination of different crop diseases are rare. The aim of the present work was to assess the capability of remote sensing in discriminating yellow rust and powdery mildew at leaf level. For each disease, a total of 30 leaf samples were collected for spectral measurement, including both infected and non-infected leaves. Prior to the analysis, the spectral data were undertaken a normalization, to minimize the spectral difference caused by the cultivars. The spectra of normal leaves were compared with that of both infected ones (yellow rust and powdery mildew) through an independent t-test. Within the bands that were significantly different between normal and diseased leaves, a further band selection was conducted to differentiate powdery mildew from yellow rust using the same independent t-test. Only those disease sensitive bands that have the discriminative power were retained. Their discriminative capability was examined by a fisher linear discrimination analysis (FLDA). It turned out that the discrimination model yielded satisfactory estimation of sample categories, with an overall accuracy over 0.9. Therefore, it is evident that the hyperspectral remote sensing is a promising way to discriminate yellow rust and powdery mildew.
机译:黄锈病(Puccinia striiformis f。sp。Tritici)和白粉病(Blumeria graminis)是严重危害世界各地冬小麦产量和谷物品质的两种严重疾病。由于它们的优选栖息地条件相似,因此两种疾病在野外同时发生的可能性很高。为了便于区分控制程序(即使用不同的杀菌剂),必须区分黄锈和白粉病。作为一种实时获取植物状态信息的快速且无损的技术,遥感技术已成功应用于几种情况下的作物病害监测。但是,针对不同作物疾病的歧视的研究很少。本工作的目的是评估遥感技术在叶片水平上识别黄锈和白粉病的能力。对于每种疾病,总共收集了30个叶片样品进行光谱测量,包括感染和未感染的叶片。在分析之前,对光谱数据进行归一化,以最小化由栽培品种引起的光谱差异。通过独立的t检验,将正常叶片的光谱与两种被感染叶片(黄锈和白粉病)的光谱进行比较。在正常叶片和患病叶片之间明显不同的条带内,使用相同的独立t检验对另一条带进行选择,以区分白粉病和黄锈病。仅保留那些具有区分力的疾病敏感频段。通过Fisher线性判别分析(FLDA)检验了它们的判别能力。事实证明,该判别模型对样本类别产生了令人满意的估计,总体准确性超过0.9。因此,很明显,高光谱遥感是区分黄锈和白粉病的一种有前途的方法。

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