Day lily field management optimization method and system based on big data

Through the comprehensive monitoring and data fusion technology of multiple sensors, combined with big data management and machine learning algorithms, the problems of real-time monitoring and data collection in daylily field management are solved, and intelligent monitoring of daylily growth environment and decision-making support for scientific planting management are realized.

CN119991333AActive Publication Date: 2025-05-13达州市农业科学研究院
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Patent Information

Application Number
CN202510068957.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-16
Publication Date
2025-05-13
Estimated Expiration
2045-01-16

AI Technical Summary

Technical Problem

There are difficulties in real-time monitoring and data collection in the field management of daylily, including complex and changeable growth environment, sensor anti-interference and environmental adaptability, long-term and stable power supply and data transmission requirements, as well as the effective integration and management of massive monitoring data.

Method used

Multiple types of sensors are used for comprehensive monitoring, multi-source heterogeneous data is integrated through sensor fusion technology, a big data management system is built for data preprocessing and analysis, a machine learning algorithm is used for prediction and decision optimization, and a growth supervision visualization system is developed.

Benefits of technology

It has realized intelligent monitoring of the growth environment of daylily, accurate prediction of growth status and decision-making support for scientific planting management, and improved the scientific and intelligent level of daylily planting.

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Abstract

The invention discloses a day lily field management optimization method and system based on big data, and the method comprises the steps: obtaining environment monitoring data of a day lily field, carrying out the mining of the environment monitoring data through big data analysis, obtaining regulation and control data, and guiding the environment regulation and control of day lily according to the regulation and control data. Predicting the environment monitoring data through a machine learning algorithm to obtain an expected growth condition of the day lily; the method comprises the following steps: acquiring meteorological data and image data of day-lily buds, obtaining disease data and growth conditions according to the image data, and predicting the growth stage and yield of the day-lily buds according to the meteorological data, the environmental monitoring data, the disease data and the growth conditions. And performing identification and optimization according to the growth stage, the yield, the environment monitoring data and the disease data through a rule engine to obtain a regulation and control scheme so as to regulate and control field management of the day lily again.
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Citation Information

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