A method and system for dynamically predicting the content of vomitoxin in wheat harvest period

Abstract

The invention provides a method and system for dynamically predicting the content of vomitoxin in wheat harvest period. Based on the historical data, the comprehensive factors suitable for establishing the prediction model are selected through the particle swarm optimization algorithm, and the prediction model is established through the comprehensive factors; The model predicts the second flowering period and the second harvest period of wheat in the current year, and then obtains the weather forecast based on the second flowering period and the second harvest period, and converts the weather forecast and geographical data into related factors. Finally, the forecast model and Related factors predict harvest-time vomitoxin levels in wheat. Compared with the existing technology, the statistical items in the prediction model are more complete. Based on the growth period index, the growth data of the year can be dynamically predicted, and then the prediction model can be continuously adjusted and established. At the same time, the particle swarm optimization algorithm is selected for multi-dimensional and large-scale data. The time overhead is more advantageous, and the prediction model established by the multiple linear regression algorithm is more accurate.

Description

technical field [0001] The invention relates to the field of agricultural prediction, in particular to a method and system for dynamically predicting the content of vomitoxin in wheat harvest period. Background technique [0002] DON pollution in grains is produced in two stages, prenatal and postnatal respectively. With the advancement of science and technology and the continuous improvement of grain storage methods in my country, the occurrence of mycotoxins in grains mainly exists before grain harvesting. Before harvest, due to fungal infection during the growth of food crops in the field, the influence of farming methods and unstable weather conditions, mycotoxins accumulate, and there are large differences between different regions and different years. Therefore, it is very important to predict the content of DON in grains before harvest, so as to implement targeted preventive measures and fundamentally reduce the contamination of DON in grains. [0003] In the prior a...

AI Technical Summary

Problems solved by technology

[0004] In the prior art, there is still a static prediction method that only predicts a single time point, which often leads to inaccurate prediction results

[0005] In the prior art, there is also the problem that the time overhead of the algorithm adopted for multi-dimensional and large-scale data is too large

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