Wheat scab prediction method based on deep forest algorithm

Abstract

The invention provides a wheat scab prediction method based on a deep forest algorithm. The wheat scab prediction method based on the deep forest algorithm comprises the following steps: step S1: collecting, sorting and summarizing a database of wheat scab occurrence conditions over the years, screening out a wheat scab occurrence process by utilizing a feature selection algorithm, and combining experience of plant protection experts to perform theoretical demonstration analysis and correction; s2, in the process of feature selection, performing analysis according to different periods of wheat growth, and finally determining that three key periods exist in the process of wheat growth. The wheat scab prediction method based on the deep forest algorithm provided by the invention has the advantages that the model is continuously optimized and corrected, so that the timeliness and the reliability of the wheat scab prediction method are greatly improved, the prediction depending on professionals is liberated, experience is combined with a scientific prediction model, and the invention has higher application value in a large range.

Description

technical field [0001] The invention relates to the technical field of wheat scab, in particular to a method for predicting wheat scab based on a deep forest algorithm. Background technique [0002] Wheat is one of the main food crops in our country. Wheat will be infected by many diseases during the growth process, among which head blight is the main one. The disease has a wide range of diseases and a large damage area, which directly affects the yield and quality of wheat. , Bringing a huge threat to the country's food security and causing direct economic losses to the country's farmers. [0003] Scientifically and effectively predicting the occurrence trend of scab in time can effectively take control measures in advance to avoid large-scale outbreaks of scab. At present, most of the prediction inventions of scab are based on empirical models for judgment and prediction , its timeliness and scope are limited to a certain extent, it cannot be applied on a large scale, it ...

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Problems solved by technology

[0003] Scientifically and effectively predicting the occurrence trend of scab in time can effectively take control measures in advance to avoid large-scale outbreaks of scab. At present, most of the prediction inventions of scab are based on empirical models for judgment and prediction , its timeliness and scope are limited to a certain extent, it cannot be applied on a large scale, it is highly dependent on people, and there are certain limitations in efficiency

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