基于来自新鲜叶片近距离反射图像的模型反演生化变量检测稻瘟病

摘要：稻瘟病是世界上最具破坏性的农作物病害之一。尽管先前的遥感研究已经研究了叶片和冠层水平的光谱变化以响 应疾病严重程度，但其数据的非成像性质使得难以检查与叶片内疾病相关的光谱变化。本研究建议使用近距离成像光谱 数据监测单个叶片上水稻叶瘟的空间和时间模式。从不同感染阶段的病叶获取高光谱图像。将图像数据转换为反射立方 体，然后使用模型反演算法 PROCWT 进行处理以检索叶片生化变量。检查生化图以研究叶片内空间变化和叶片级时间 变化。初步结果表明 PROCWT 算法可以执行反射图像立方体。检索到的叶绿素图显示，随着感染阶段的进展，叶绿素 含量下降，并且叶片内的空间模式对疾病有显著影响。

关键词：稻瘟病、高光谱图像、叶绿素含量、PROCWT

期刊：IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

DOI：10.1109/IGARSS.2018.8519229

设备：GaiaField

DETECTING RICE BLAST DISEASE USING MODEL INVERTED BIOCHEMICAL VARIABLES FROM CLOSE-RANGE REFLECTANCE IMAGERY OF FRESH LEAVES

Abstract：Rice blast is one of the most devastating crop diseases around the world. Although previous remote sensing studies have examined the spectral variation at leaf and canopy levels in response to disease severity levels, the non imaging nature of their data makes it difficult to examine the spectral variation related to the disease within a leaf. This study proposes to monitor the spatial and temporal pattern of rice leaf blast on individual leaves with close-range imaging spectroscopy data. Hyperspectral images were acquired from diseased leaves at different infection stages. The image data were converted to reflectance cubes and then processed with a model inversion algorithm PROCWT to retrieve leaf biochemical variables. The biochemical maps were examined to investigate the within-leaf spatial variation and leaf-level temporal variation. Preliminary results demonstrated that the PROCWT algorithm could perform on reflectance image cubes. The retrieved chlorophyll maps exhibited a decline with infection stage and significant within-leaf spatial patterns in response to the disease.

Keywords：Rice blast disease, Hyperspectral imagery, Chlorophyll content, PROCWT