Risk assessment method for wide-range crop diseases

Abstract

The invention discloses a risk assessment method for wide-range crop diseases. The method comprises the following steps that: selecting the n pieces of meteorological parameters of a monitoring area,giving an attack earlier stage meteorological early waning, carrying out meteorological encryption monitoring, calculating each meteorological parameter to obtain a disease occurrence posterior probability, predicting a meteorological multi-factor disease probability, adopting an inverse distance weighted method to carry out spatial interpolation, generating an accumulated meteorological risk map,generating a remote sensing growth vigor map, and generating a disease occurrence appropriateness map. The method has the characteristics that comprehensive information, including meteorology, crop habitats, representation after crops are stressed and the like, can be comprehensively considered for giving a disease early warning, and the method can be reused by various main crop diseases.

Description

technical field [0001] The invention relates to the technical field of meteorological and aerial data analysis and processing, in particular to a risk assessment method for large-scale crop disease occurrence based on meteorological time-series dynamic analysis and crop growth monitoring. Background technique [0002] Crop diseases and insect pests are important biological disasters in agricultural production, and have always been one of the main factors restricting agricultural high-yield, high-quality, high-efficiency, ecology, and safety. In recent years, global climate change has intensified, and various crop diseases have become more frequent, which has brought huge challenges to global food production and security. [0003] According to statistics from the Food and Agriculture Organization of the United Nations, 10% of the world's grain production is lost due to pests and 14% due to diseases. Therefore, early warning of large-scale crop diseases is of great significan...

AI Technical Summary

Problems solved by technology

[0004] At present, crop disease early warning often does not consider other pathogenic factors, and only relies on meteorological data to make predictions in a large area, resulting in low prediction accuracy and unable to guide actual field operations.

Although there are also methods for crop disease prediction based on meteorological data and remote sensing data, these methods only use meteorological data and remote sensing data as two types of factors in the disease prediction model to model, and do not fully consider the role of continuous meteorological data in disease prediction. Influence on the occurrence and development of disease during the occurrence process

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