Agricultural Field Resource Forecasting With Availability-Based Allocation
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Solution Overview
Problem
Existing agricultural assistance systems fail to optimize resource allocation based on the specific needs and properties of a field, neglecting the variety of pesticide effectiveness and cost-effectiveness, and do not account for user-specific requirements.
Innovation Solution
An assistance system comprising a database and forecasting unit that uses historical, current, and expected field data to determine a resource forecast, optimizing resource allocation based on predefined criteria, including yield and profit forecasts, while considering environmental compatibility and resource availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional assistance systems use satellite data and plant growth models to monitor resource use, then field monitoring capability is provided, but resource allocation optimization is not achieved
Solution Approach 1:
The system segments resource management into multiple dimensions: resource types (pesticides, fertilizers, machinery), resource units (specific products, equipment), and optimization criteria (yield, cost, environment). This segmentation allows comprehensive optimization without overwhelming system complexity.
Solution Approach 2:
The system performs preliminary actions by determining resource forecasts before actual agricultural activities. It predicts resource requirements based on planning specifications and field data, allowing users to optimize resource allocation in advance rather than reactively.
2Ease of operation
If resource data specifies availability of resource units, then resource availability is ensured, but selection optimization based on user needs and field properties is reduced
Solution Approach 1:
The system dynamically adjusts resource selection based on multiple factors including user-specific needs, field properties, optimization criteria, and resource availability. The resource list is not static but adapts to changing conditions and user requirements, providing both ease of operation and high adaptability.
Solution Approach 2:
The system changes parameters by considering multiple optimization criteria (yield forecast, cost forecast, environmental compatibility) simultaneously. It adjusts resource unit selection based on varying weights and priorities of these parameters, allowing flexible adaptation to different user needs and field conditions.
3Measurement precision
If multiple optimization criteria are considered for resource allocation, then resource selection quality is improved, but computational complexity increases
Solution Approach 1:
The system handles multiple optimization criteria by treating them as adjustable parameters with different weights. It changes the parameter representation to include yield forecasts, cost forecasts, and environmental compatibility scores, allowing precise resource forecasting while managing computational complexity through structured parameter organization.
4Productivity
If resource units are assigned based on optimization criteria, then resource allocation is optimized, but resource availability may be compromised
Solution Approach 1:
The system incorporates feedback by continuously monitoring resource availability status and adjusting the resource list accordingly. It provides feedback to users about which resource units are available and makes adjustments to balance optimization goals with actual availability constraints, ensuring both efficiency and reliability.
Data Source
Figure 1
AI summary
The invention relates to an assistance system (1) for determining a resource forecast for an agricultural field (F), wherein the assistance system (1) comprises a database (2) and a forecasting unit (3), wherein the database (2) includes historical field data (4), current field data (5) and expected field data (6) of the agricultural field (F), wherein the field data (4, 5, 6) include field condition data and field environment data, wherein the forecasting unit (3) determines a resource forecast based on a planning specification (8) and depending on the field data (4, 5, 6) using a predetermined forecasting model (9), which represents the expected need for agricultural resources to carry out agricultural activities on the field (F) in accordance with the planning specification (8).It is proposed that the database (2) includes resource data which specifies the availability of resource units (10, 11) for providing the resources, and that the forecasting unit (3) assigns resource units (10, 11) which are specified as available in the resource data to the determined resource forecast based on a predefined optimization criterion in a resource list (12).