Turfgrass Growth Prediction via Meteorological Data
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Solution Overview
Problem
Current turfgrass maintenance operations, such as cutting and irrigation, are often ineffective or counterproductive when performed by non-specialized individuals, leading to issues like fungal proliferation, rot, and excessive resource consumption, and lack automation and intelligence in scheduling.
Innovation Solution
A method that uses geographic location, meteorological parameters, and grass type to predict turfgrass growth, optimizing maintenance operations by scheduling devices like robotic lawnmowers and irrigation systems, and providing users with intuitive suggestions for maintenance through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated maintenance operations are implemented, then productivity and resource optimization improve, but device complexity increases
Solution Approach 1:
A cloud-based prediction service acts as an intermediary between meteorological data sources and maintenance devices. The service receives meteorological parameters, calculates grass growth predictions, and sends recommendations to maintenance devices, thereby simplifying the overall system architecture while enabling intelligent automated maintenance
Solution Approach 2:
The system enables maintenance devices to autonomously determine optimal maintenance schedules based on growth predictions. The robotic lawnmower and irrigation system can self-adjust their operations according to received growth predictions without requiring complex centralized control
2Ease of operation
If maintenance operations are performed by non-specialized individuals, then ease of operation improves, but manufacturing precision deteriorates
Solution Approach 1:
The system incorporates feedback loops where maintenance devices report their operations back to the prediction service. The service adjusts future growth predictions and maintenance recommendations based on actual maintenance outcomes, enabling non-specialized users to achieve professional-quality results through data-driven guidance
Solution Approach 2:
The system performs preliminary calculations of grass growth predictions before maintenance operations are executed. Users receive advance recommendations on when and how to perform maintenance, eliminating the need for specialized knowledge during actual maintenance execution
3Reliability
If frequent maintenance operations are performed, then reliability of turfgrass health improves, but loss of time and energy increase
Solution Approach 1:
The system dynamically adjusts maintenance frequency based on real-time growth predictions. Instead of fixed schedules, the robotic lawnmower and irrigation system adapt their operation frequency to actual grass growth rates, performing maintenance only when necessary to maintain health standards
Data Source
Figure 1~2A
Figure 2B~2C
Figure 3A
AI summary
The present invention relates to a method for obtaining at least one predictive piece of information relating to a turfgrass. In a first step of the method, the geographic location of the turfgrass is established. In a second step of the method, a time span of interest is defined. In a third step of the method, one or more meteorological parameters related to the geographic location are acquired, for each day of said time span. In a fourth step of the method, the at least one predictive piece of information is calculated on the basis of the one or more meteorological parameters related to the geographic location of the turfgrass. The at least one predictive piece of information comprises the estimated growth of said turfgrass on each day of the time span of interest. According to a preferred embodiment of the present invention, the one or more meteorological parameters comprise an expected average temperature.