http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113884444-A

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classificationCPCAdditional http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2021-1765
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classificationCPCInventive http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N21-25
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filingDate 2020-07-03-04:00^^<http://www.w3.org/2001/XMLSchema#date>
inventor http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f6cf82869e0bd588840d6b38e25024b9
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publicationDate 2022-01-04-04:00^^<http://www.w3.org/2001/XMLSchema#date>
publicationNumber CN-113884444-A
titleOfInvention Model establishing method, SPAD value predicting method and device and electronic equipment
abstract The application relates to a model establishing method, a method and a device for predicting an SPAD value and electronic equipment, and belongs to the technical field of crop monitoring. The method comprises the following steps: acquiring remote sensing images of the same crop in different areas and corresponding measured values of canopy leaves SPAD; aiming at each area, extracting spectral reflectivity from the remote sensing image corresponding to the area, and calculating a vegetation index based on the spectral reflectivity; and establishing an SPAD value inversion model of the crop by using the spectral reflectivities of different areas, the corresponding vegetation indexes and the SPAD measured values of the canopy leaves. By acquiring remote sensing images of crops in different areas and in the same kind and measuring the SPAD value of the crop canopy leaf, a general inversion model for predicting the SPAD value of the crop canopy leaf, which is suitable for monitoring data of sensors in different areas and in different types, is established, so that the problems that the existing measurement method needs to enter field actual measurement, time and labor are wasted, and the requirement for rapid monitoring of the crops cannot be met are solved.
priorityDate 2020-07-03-04:00^^<http://www.w3.org/2001/XMLSchema#date>
type http://data.epo.org/linked-data/def/patent/Publication

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Total number of triples: 20.