Micro-Climate Mapping with Homogeneous Plot Grouping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current precision agriculture systems face challenges in interpreting sensor data to determine homogeneous micro-climate variations across large fields, due to non-uniform sensor deployment, missing values, and limited sensor reliability, which affects crop management and local weather forecasting.
Innovation Solution
A processor-based method and system that collects sensor data from multiple plots, groups sensors following the same trend, performs homogeneity checks, and generates a micro-climate view by regrouping plots to create homogeneous groups, enabling accurate micro-climate forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are deployed pervasively across large fields to capture micro-climate variations, then measurement precision and data coverage are improved, but device complexity and deployment cost increase
Solution Approach 1:
The patent segments the large field into multiple homogeneous groups based on sensor data analysis. Sensors are virtually grouped into clusters representing different micro-climate zones, allowing precise local measurements without requiring physical deployment of sensors at every location. This segmentation approach captures micro-climate variations while reducing the effective number of sensors needed.
Solution Approach 2:
The patent creates a multi-functional system where a limited set of sensors serves multiple purposes: they not only measure local conditions but also contribute to characterizing entire homogeneous groups. Each sensor's data is used both for its immediate location and for inferring conditions in similar zones, maximizing the utility of each sensor deployment.
2Device complexity
If sensors are deployed at select locations to reduce deployment complexity, then device complexity is reduced, but measurement precision and micro-climate coverage deteriorate
Solution Approach 1:
The patent creates virtual copies of sensor measurements by identifying homogeneous groups. When sensors are deployed at select locations, their measurements are used to characterize entire groups of similar zones. The system effectively copies the measurement information from deployed sensors to represent undeployed but similar locations, maintaining measurement precision without additional physical sensors.
Solution Approach 2:
The patent transitions from physical sensor deployment density to data-based spatial characterization. Instead of improving coverage by adding more physical sensors in space, the system uses data processing and grouping to create a comprehensive micro-climate model, adding an informational dimension that compensates for physical deployment limitations.
3Quantity of substance
If inexpensive sensors are used to reduce investment cost, then cost is reduced, but reliability and data accuracy deteriorate
Solution Approach 1:
The patent merges data from multiple inexpensive sensors to achieve reliable measurements. By grouping sensors and analyzing trends across groups, the system compensates for individual sensor inaccuracies. The collective data from multiple low-cost sensors provides more reliable micro-climate characterization than a single expensive sensor would.
Solution Approach 2:
The patent implements feedback mechanisms where sensor data is continuously analyzed for consistency and trends. Homogeneous groups are validated against each other, and inconsistent readings are identified and corrected. This feedback loop maintains data reliability even when using inexpensive sensors with higher individual error rates.
4Device complexity
If regional-level forecasts are used for open farms without dedicated weather stations, then device complexity is reduced, but measurement precision and local accuracy deteriorate
Solution Approach 1:
The patent applies local quality by creating distinct homogeneous groups that represent different micro-climate zones within the farm. Each group receives tailored forecasts based on its specific characteristics rather than applying a single regional forecast to the entire farm. This ensures local accuracy while using a distributed, low-complexity sensor network.
Solution Approach 2:
The patent segments the farm into multiple micro-climate zones based on sensor data patterns. Instead of using a single regional forecast for the entire farm, the system creates localized forecasts for each homogeneous group, capturing local variations in temperature, humidity, and other parameters that regional forecasts would miss.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
State of the art systems used for monitoring of land (for example, agricultural land), fail to accurately assess various conditions in the land area and make predictions. The disclosure herein generally relates to agricultural systems, and, more particularly, to a method and system for micro-climate management in a land area being monitored. The system groups the different plots based on sensor trend information and based on a determined homogeneity information. A micro-climate view of the land area is accordingly generated, which in turn is used to generate micro-climate predictions.