Intelligent identification method and system for forestry diseases and insect pests

Abstract

The invention provides an intelligent identification method and system for forestry diseases and insect pests in the field of forestry disease and insect pest monitoring. The intelligent identification method comprises the steps that S10, basic information of forests in a regional scale range is acquired; S20, acquiring a first spectral reflectance from the satellite remote sensing data; obtainingclimate data from the meteorological data; S30, acquiring basic information under the current time phase based on the climate data, comparing the basic information with the first spectral reflectance, judging whether the physiological characteristics are normal or not, and if so, ending the process; if not, identifying a suspected area, acquiring remote sensing data of the suspected area by usingthe unmanned aerial vehicle, and generating a forest resource image; S40, obtaining a second spectral reflectivity from the forest resource image; and S50, comparing the basic information under the current time phase with the second spectral reflectance, judging whether the physiological characteristics are normal or not, and if so, ending the process; if not, generating a forestry pest and disease damage judgment report. The intelligent identification method and system for forestry diseases and insect pests have the advantage that the precision of forest pest monitoring is greatly improved.

Description

technical field [0001] The invention relates to the field of monitoring forestry diseases and insect pests, in particular to an intelligent identification method and system for forestry diseases and insect pests. Background technique [0002] Accurately identifying forestry diseases and insect pests is the basis for dynamic monitoring of forest trees, revealing the physiological growth laws of forest trees, and scientifically guiding prevention and control. The current detection and identification of forestry pests and diseases mainly rely on a small number of forestry experts and forest technicians to complete, that is, mainly rely on visual inspection (morphological characteristics of pests and diseases, such as color, size, shape, etc.), forest sampling and other methods for manual identification . However, there are many deficiencies in the human visual system, such as limitations, ambiguity, subjectivity, lack of persistence, etc., which greatly reduce the accuracy of ...

AI Technical Summary

Problems solved by technology

Early remote sensing data, such as the sensor LandastTM carried by the subject imager and the satellite sensor MODIS, cannot meet the high spatial resolution and time resolution at the same time, which constitutes a certain hardware condition obstacle for the monitoring of pests and diseases at the regional scale. Some forestry pest monitoring based on satellite imagery often only considers spectral information, but does not consider temporal information, which is very important for forestry pest monitoring, and there is a large uncertainty in the monitoring results

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