Neural Network Field Access Readiness Probability System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Agricultural machines face inaccuracies in determining field access readiness due to reliance on personal experiences and weather predictions, leading to inefficient field access and machine deployment, resulting in financial and effort losses.
Innovation Solution
A system utilizing a behavioral decision database and neural networks to assess field and weather metrics, providing a probability of field access readiness and generating machine plans for optimal field operations, leveraging historical data from global farming market participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personal experiences and weather predictions are used to determine field access readiness, then decision-making is simple and quick, but accuracy and reliability are poor
Solution Approach 1:
The patent introduces a server-based intermediary system that collects data from multiple sources (weather services, field sensors, agricultural databases) and processes it through neural networks to generate field access readiness probabilities. This intermediary handles the complexity of data integration and analysis, allowing individual farming operations to benefit from accurate predictions without maintaining complex systems themselves.
Solution Approach 2:
The system provides multiple functions through a single platform: weather prediction, field condition monitoring, historical data analysis, and machine deployment optimization. This universal system serves multiple purposes that would otherwise require separate tools and expertise, improving accuracy while keeping the user-side interface simple.
2Productivity
If field access readiness is determined accurately using comprehensive data analysis, then machine deployment efficiency improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data collection, processing, and model training in advance. Historical field data, weather patterns, and soil conditions are continuously analyzed before they are needed for decision-making. This allows the neural network to provide rapid predictions when field access decisions are required, without experiencing processing delays at the moment of decision.
Solution Approach 2:
The system incorporates feedback loops where actual field conditions and machine performance data are continuously fed back into the neural network. This allows the model to learn from real-world outcomes and improve its predictions over time, increasing productivity while the feedback processing occurs asynchronously rather than blocking real-time operations.
3Reliability
If global agricultural data is integrated to improve decision-making accuracy, then field access determination reliability increases, but information processing complexity and data management requirements increase
Solution Approach 1:
The server acts as an intermediary that manages the complexity of integrating global agricultural data. It standardizes data formats from different sources, handles data quality issues, and processes information through centralized neural networks. This allows individual users to access reliable, globally-informed predictions without directly managing the complexity of global data integration.
Solution Approach 2:
The system merges multiple data sources (weather services, field sensors, historical records, soil databases) into a unified analysis framework. By combining these diverse data streams through a single neural network system, the patent achieves high reliability while consolidating data management complexity into a centralized platform rather than distributing it across multiple user systems.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to determine field access readiness to receive in a neural network a field metric, receive in the neural network a first field operating decision, generate from the neural network a field condition at a time of operation based on the field metric, generate a first probability that a field may be accessed given the field condition at the time of operation based on the first field operating decision and the field condition at the time of operation; and apply the first probability to the field, or subset areas of the field by the field metric.