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全文摘要次数: 61 全文下载次数: 37
引用本文:

DOI:

10.11834/jrs.20243413

收稿日期:

2023-09-27

修改日期:

2024-04-24

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两类真菌病害的特异性叶片光谱响应与监测精度对比研究
薛博文, 孔媛媛, 田龙, 王雪, 姚霞, 朱艳, 曹卫星, 程涛
1. 南京农业大学国家信息农业工程技术中心/智慧农业教育部工程研究中心/农业农村部作物系统分析与决策重点实验室/江苏省信息农业重点实验室/现代作物生产省部共建协同创新中心 南京 210095
摘要:

作物病害遥感监测对精准防控和粮食安全至关重要。病害光谱响应规律及其生理依据是病害遥感监测的基础,而针对该基础性问题的深入研究仍较缺乏。本研究通过比较两类侵染特性相异的真菌病害及其光谱监测精度,旨在系统阐明病叶光谱响应机制及其特异性。研究选择活体营养型小麦白粉病(Wheat powdery mildew, WPM)和半活体营养型水稻叶瘟病(Rice leaf blast, RLB)作为实例。基于叶片反射光谱和辐射传输模型参数反演,本文对比了两类病害的光谱响应、生化参数与结构参数变化规律;并在病叶识别和病情严重度估算中比较病害光谱特征的精度差异及其特异性监测表现。结果表明,两类病害的光谱变异趋势整体相似但其光谱响应强度有显著差异,且两者在绿峰及近红外平台的光谱形状变化明显不同。此外,色素含量对两类病害响应趋势相似但强度不同,其中叶片含水量和结构参数仅对RLB响应显著。在病叶识别中,前人构建病害光谱特征均对目标病害监测表现出特异性优势。其中,小波特征WF3,820和WF5,866分别对WPM和RLB的识别精度最高且特异性最强。在病情估算中,光谱指数RIBIred和PRI分别对WPM和RLB病情严重度敏感性最高且特异性显著。其中,RIBIred在病叶识别和病情估算中的综合表现和特异性最优(Overall accuracy = 0.74,R2 = 0.58)。本文将两类病害光谱响应与病理成因关联分析,为病害特异性光谱监测提供了有力证据和新的认识,对病害光谱监测机理解析和多种病害识别具有重要意义。

Comparison between two types of fungal diseases in specific spectral responses and monitoring performance
Abstract:

Abstract: Remote sensing monitoring of crop diseases plays a crucial role in food security in terms of the precision management of chemical fungicides and the efficient assessment of crop losses. Spectroscopic detection of disease infection has been investigated for numerous crop diseases individually. However, it remains unclear how biochemical and spectral variations differ in response to divergent diseases given the distinct symptoms caused by different pathogens. Objective: This study aimed to determine the pathological mechanism and specificity of the spectral responses of two types of fungal diseases by comparing their specific spectral signatures and disease monitoring performance. Methods: The biotrophic wheat powdery mildew (WPM) and the semi-biotrophic rice leaf blast (RLB) diseases were used as examples for the comparison. With the reflectance measurements of infected leaves and radiative transfer modeling, a comparative analysis for these two diseases was conducted in terms of spectral responses, leaf biochemical and structural parameters. Additionally, we assessed the specificity of various disease-related spectral features, which were proposed in previous studies for the monitoring of WPM or RLB, by accuracy comparison in the detection of diseased leaves and the estimation of leaf lesion proportion (LLP). Results: The results showed significant differences in the intensity of spectral responses to the two diseases despite the similarity observed in the general trend in spectral variations. In addition, distinct variations appeared in the spectral shape at the green peak and near-infrared plateau between WPM and RLB. Moreover, the pigment variations in response to two infections were generally similar, whereas the response was more pronounced for RLB. Notably, the leaf water content and structural parameter displayed significant changes only in relation to the severity of RLB. In disease detection, the spectral features developed for WPM or RLB generated higher accuracy in detection of the target disease than the other disease. Wavelet features of WF3,820 and WF5,866 displayed the highest accuracy and specificity for WPM and RLB, respectively. Regarding the severity quantification, most spectral features exhibited higher sensitivity to the LLP of RLB than to that of WPM. Specifically, a variat of rice blast index (RIBIred) and the photochemical reflectance index (PRI) demonstrated the highest accuracy and specificity in the LLP estimation of WPM and RLB, respectively. Among the WPM- or RLB-related spectral features, RIBIred showed the optimal monitoring performance and specificity in both disease detection and severity estimation (Overall accuracy = 0.74, R2 = 0.58). Conclusions: Our findings provide solid evidence and new insights into disease-specific spectroscopic monitoring by associating spectral responses with pathogenesis of two types of fungal diseases. This study offers significant contributions to the understanding of disease monitoring mechanisms and the identification of multiple diseases with hyperspectral remote sensing.

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