Farm intelligence method and device and control equipment

Abstract

The invention provides a farming intellectualization method and device and control equipment, and the method comprises the steps: obtaining the real-time monitoring data of a target land parcel in the current growth season of a planted crop based on a yield estimation request in the growth season of the crop, obtaining the corresponding historical monitoring data in a time interval not covered by the real-time monitoring data, and obtaining the real-time monitoring data of the target land parcel in the current growth season of the crop; according to the real-time monitoring data, the historical monitoring data and the yield estimation model of the target land parcel, crop yield estimation is performed to determine the estimated yield of the target land parcel in the current growing season, and crop yield estimation can be dynamically performed in the whole growing season in real time; and according to the estimated yield of the target land parcel in the current growth season, the historical yield information of the crops, the real-time monitoring data and the agricultural knowledge graph information, the first farming task for the target land parcel is issued, so that farming operation can be performed on the target land parcel with low estimated yield in time when the estimated yield is low; the crop yield of a target plot can be greatly improved.

Description

technical field [0001] The present application relates to the technical field of agricultural intelligence, in particular to a method, device and control equipment for agricultural intelligence. Background technique [0002] Crop yield estimation has always been an important scientific issue in agricultural science research. In crop yield estimation methods, crop models based on crop photosynthesis, respiration, transpiration, nutrition and other mechanism processes can accurately simulate the continuous evolution of crop objects in time and space by relying on their inherent physical processes and dynamic mechanisms, and can accurately Simulating the growth and development status and yield of single-point crops is one of the methods commonly used by agricultural research experts. [0003] With the development of agricultural technology, agricultural operations are becoming more and more intelligent. In the current agricultural intelligent platform, the crop yield estimati...

AI Technical Summary

Problems solved by technology

However, when crop yield estimation methods based on crop models are applied to crop yield estimation at large spatial scales (such as counties, cities, or even larger regions), due to the heterogeneity of the surface and near-surface environments, some macroscopic data in the crop model It is very difficult to obtain and regionalize parameters, and sampling takes a long time. Usually, one sampling and yield estimation is carried out when the crop is close to maturity, and it is impossible to dynamically estimate crop yield during the entire growth period of the crop, which is not conducive to improving crop yield. , when the estimated output is low, it is necessary to manually analyze the cause of the estimated low output and manually adjust the farming tasks, the degree of intelligence is low

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