Fusarium infection on wheat is a widespread thread for humans' and animals' nutrition as these fungi are known to produce the highly dangerous mycotoxin deoxynivaleol (DON). Besides this, Fusarium also induces head blight, a disease resulting in huge economic losses due to shrivelled and low mass or dead kernels. Early disease detection could help to reduce yield losses and health threads from DON contamination. The potential of chlorophyll fluorescence imaging (CFI) to yield these aims was investigated in laboratory experiments applying a FluorCam 700MF commercial CFI system. Healthy (control) plants as well as plants artificially infected with Fusarium culmorum were rated visually according to the stage of development and the degree of disease. Subsequently, a chlorophyll fluorescence image analysis of the potential maximum photochemical efficiency (F-v/F-m) was applied to determine the degree and the distribution of the damage of the ears. Between the sixth and eleventh day after artificial inoculation photosynthetic activity of single damaged kernels of diseased ears dropped to zero. Although this only marginally affected the average maximum photochemical efficiency of entire ears, the infection led to a significant increase in the statistical distribution of F-v/F-m in the images. Pixelwise integration of FvFm-values (from low to high) of the fluorescence images allowed a differentiation, in steps of 10%, between ears of different degree of disease of 10% on in the BBCH stage 75. Lowest level of disease detection by CFI corresponded to a visually rated degree of disease of at least 5%. However, the possibility to distinguish between diseased and healthy cars became highly limited with incipient ripening of kernels and concomitant chlorophyll degradation at growth stage 81.
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National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, HefeiNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei
Huang L.
Zhang H.
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National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, BeijingNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei
Zhang H.
Huang W.
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National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing
Key Laboratory for Earth Observation of Hainan Province, SanyaNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei
Huang W.
Dong Y.
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Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, BeijingNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei
Dong Y.
Ye H.
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Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing
Key Laboratory for Earth Observation of Hainan Province, SanyaNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei
Ye H.
Ma H.
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Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, NanjingNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei
Ma H.
Zhao J.
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National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, HefeiNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei