Crop yield prediction method and device and computer readable storage medium

Abstract

The invention relates to the intelligent decision, and discloses a crop yield prediction method, which comprises the following steps of obtaining th ecrop sample data, the crop sample data comprisingplanting characteristic data representing crops and actual yield values of the crops; according to the crop sample data, obtaining a yield prediction model through training; acquiring the target cropdata; and according to the yield prediction model, predicting the target crop data, and determining a predicted yield corresponding to the target crop data. The invention further provides a crop yieldprediction device and a computer readable storage medium. The crop yield of different lands in the same region can be accurately predicted based on the historical data, and quick compensation can berealized.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a crop yield prediction method, device and computer-readable storage medium. Background technique [0002] Crop yield insurance takes the loss of farmers’ crop yield as the subject of insurance, and takes the actual yield of crops lower than the yield agreed in the contract as an insurance event. Traditional crop insurance pricing is generally based on the data expansion method of historical unit yield data and based on known The stochastic simulation method of the distribution function, these two methods are based on the overall data in the past and use the statistical method to predict, so that the output of different lands within a region can only be given the same prediction, the above methods can be used in feasible in some areas. However, when applied to crop yield prediction under different geological conditions, the effect will be unsatisfactory. Due to the di...

AI Technical Summary

Problems solved by technology

Due to the different geological conditions, climatic conditions, precipitation conditions, light conditions, nitrogen fertilizer conditions, etc. in the same area, the crop yields are also very different. The traditional overall quantitative prediction method is difficult to meet the accurate prediction of crop yields. This leads to a large deviation in crop yield insurance, so the rate is the same for high-quality land and low-quality land, and it is impossible to get a more detailed and accurate forecast for the output of a piece of land this year

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