Self-adaptive crop management system and method based on low-power-consumption sensor and Boost model

Abstract

The invention relates to a self-adaptive crop management system and method based on a low-power-consumption sensor and a Boost model. The self-adaptive crop management system comprises a crop growth environment data real-time acquisition device, a real-time data analysis processing device and a crop management decision execution device. The crop growth environment data real-time acquisition devicecomprises a low-power-consumption soil sensor, a spectrum camera and a crop growth covariable data acquisition device. The real-time data analysis processing device comprises a preprocessing server,a modeling calculation server, an experience knowledge base, a model base and a data transmission communication network. The crop management decision execution device comprises a user interaction device, a production decision support server and a decision execution mechanism. According to the method, the defects that an existing agricultural data modeling and decision-making method cannot be updated in real time according to data changes, model construction and evaluation depend on manual intervention, and models and methods are insufficient in generalization in different regions and backgrounds can be overcome.

Description

Technical field [0001] The invention relates to the technical field of agricultural intelligence, in particular to an adaptive crop management system and method based on a low-power sensor and a Boost model. Background technique [0002] In the process of agricultural planting, soil moisture, temperature and electrical conductivity are important factors affecting crop yields. Accurately grasp the size and change trend of soil temperature, water content and electrical conductivity, which is helpful for scientific management of crops. With the development of smart agriculture, more and more growers have begun to deploy various sensors to collect and utilize environmental information, and deploy integrated soil sensors to develop temperature, humidity, light, soil conditions, water quality conditions, and meteorological conditions. The monitoring of other information provides data-driven decision-making for crop planting, yield forecasting, and water-saving irrigation, ensuring tha...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the shortcomings of existing agricultural data modeling and decision-making methods that cannot be updated in real time according to data changes in complex agricultural production environments, model construction and evaluation r

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