Micro-Climate Plot Grouping for Hyper-Localized Field Forecasts
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Solution Overview
Problem
Existing precision agriculture systems face challenges in interpreting sensor data to accurately manage micro-climates across large open fields, due to issues like heterogeneous terrain, limited sensor deployment, data gaps, sensor reliability, and the need for hyper-localized forecasts.
Innovation Solution
A processor-implemented method and system for micro-climate management that collects sensor data from multiple plots, groups sensors following the same trend, regroups them based on homogeneity, and generates a micro-climate view and forecast for the land area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are deployed pervasively across the entire land area, then measurement precision and micro-climate understanding improve, but device complexity and deployment cost increase
Solution Approach 1:
The patent divides the large land area into multiple smaller plots, each monitored by dedicated sensors. This segmentation allows comprehensive micro-climate coverage without requiring a single complex centralized system, as each plot can be independently monitored and managed.
Solution Approach 2:
The system recognizes that different plots have different micro-climate characteristics and treats each plot individually with its own sensor group. This local quality approach ensures that each area is monitored with appropriate sensors tailored to its specific conditions, improving measurement precision without uniformly increasing complexity across the entire farm.
2Device complexity
If sensors are deployed at select locations only, then device complexity and cost are reduced, but measurement precision and micro-climate understanding deteriorate
Solution Approach 1:
By segmenting the farm into plots and assigning sensor groups to each plot, the system achieves comprehensive coverage through a modular approach. This reduces overall complexity compared to a fully distributed system while maintaining measurement precision through strategic placement at plot-level locations.
3Productivity
If homogeneous plots are grouped together for micro-climate management, then productivity and decision accuracy improve, but device complexity and data processing requirements increase
Solution Approach 1:
The patent merges plots with similar micro-climate characteristics into homogeneous groups. This combining reduces the overall complexity of management decisions by treating similar plots as a single unit, while still maintaining the ability to differentiate and manage distinct micro-climate zones independently.
Solution Approach 2:
The system changes the organizational parameter from individual plot management to grouped plot management based on micro-climate homogeneity. This parameter change simplifies productivity decisions by reducing the number of independent variables to manage, while maintaining accuracy through science-based grouping criteria.
Data Source
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.