Method, device and computer readable storage medium for predicting crop yield

Abstract

The invention provides a method, a device and a computer-readable storage medium for predicting crop yield, which relates to the technical field of agricultural production. The method for predicting crop yield comprises the following steps: obtaining static information of target crops in a preset area; Collecting the dynamic information of the target crops in the preset region in the current growth stage; wherein static information and dynamic information are the factors affecting the growth of the target crops; The static information and dynamic information are input into the target crop yield prediction model established in advance to obtain the predicted yield of the target crop. The technical scheme can obtain static information when the target crop is sown, Also can collect the fieldmanagement information of the target crops as the dynamic information, so as to consider the sowing situation of the target crops and the dynamic influence factors in the growth process, and also consider the influence of the target crops when subjected to natural disasters, diseases and pests, so as to accurately predict the yield of the target crops.

Description

technical field [0001] The present invention relates to the technical field of agricultural production, in particular to a method, device and computer-readable storage medium for predicting crop yield. Background technique [0002] In the prior art, traditional crop yield estimation methods mainly include agronomic forecasting methods, statistical forecasting methods, weather forecasting methods, and the like. These yield estimation methods require a large amount of manual investigation and data statistics, and are not suitable for large-scale crop yield estimation. Moreover, these yield estimation methods only consider the sowing conditions of crops, but crops are also restricted by multiple factors during the growth process, especially climate disasters, disease and pest disasters will have a serious impact on the deviation of production prediction results. Contents of the invention [0003] In order to overcome the deficiencies in the above-mentioned prior art, the pre...

AI Technical Summary

Problems solved by technology

These yield estimation methods require a lot of manual investigation and data statistics, and are not suitable for large-scale crop yield estimation.

Moreover, these yield estimation methods only consider the sowing conditions of crops, but crops are also restricted by multiple factors during the growth process, especially climate disasters, disease and pest disasters will have a serious impact on the deviation of production prediction results

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