Model-Based Field Boundaries for Multi-Factor Route Planning
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Solution Overview
Problem
Existing route planning systems for mobile machines fail to consider complex interactions between terrain, ground conditions, soil type, weather conditions, and machinery capabilities, leading to suboptimal paths and increased operational costs.
Innovation Solution
A system utilizing machine learning and deep learning models, such as RNNs or LSTM networks, to generate accurate field boundaries and routes by combining satellite images with historical GPS data, considering multiple factors like terrain, ground conditions, soil type, and machinery capabilities, and generating model-based bounding boxes, waylines, and headlands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual planning or simple heuristic algorithms are used for route planning, then the system complexity is low and ease of operation is maintained, but the route planning accuracy and productivity are suboptimal due to not considering complex interactions between terrain, ground conditions, soil type, weather conditions, and machinery capabilities
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that processes multiple input factors (terrain, ground conditions, soil type, weather conditions, machinery capabilities) and generates optimized route plans. This intermediary handles the complexity of considering all interactions between factors, allowing the overall system to achieve high productivity without requiring the end user to directly manage the complex interactions.
Solution Approach 2:
The patent replaces traditional mechanical/heuristic route planning methods with an intelligent system based on machine learning models. Instead of using simple algorithms that manually consider each factor, the system uses trained neural networks to automatically process and integrate multiple factors, substituting computational intelligence for traditional mechanical decision-making processes.
2Measurement precision
If farmers create field boundaries manually using satellite images, then no database is required and adaptability is maintained, but the measurement precision and reliability of field boundaries are inaccurate leading to inefficient waylines and routes
Solution Approach 1:
The system performs self-service by automatically generating accurate field boundaries using machine learning models that process satellite imagery and historical data. Instead of requiring farmers to manually delineate boundaries, the system autonomously identifies and creates precise boundary definitions, improving measurement precision while reducing the complexity of the boundary creation process for end users.
3Loss of energy
If suboptimal paths are generated due to not considering complex interactions, then the ease of operation is maintained through simpler planning methods, but the loss of energy increases due to increased fuel consumption and operational costs
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models with extensive data about terrain, ground conditions, soil type, weather conditions, and machinery capabilities. This preliminary training enables the system to make energy-efficient routing decisions without requiring complex real-time calculations during operation, reducing fuel consumption while managing system complexity through offline model preparation.
Data Source
AI summary
Technologies for generating field boundaries. In some embodiments, a method includes receiving, by a computing system, mobile machine location information. The information including a series of time-stamped locations of a mobile machine as it moves through an area of land during a time period. The machine including an implement used for farming, construction, or forestry. The method also including receiving, by the system, satellite images of the area. The method also including using, by the system, a machine learning model to generate a model-based bounding box for the area. The method can also include generating, by the system, a graphic of the box. The box graphic can be generated within a graphical mapped area of land. And, the method can include displaying, via a GUI, the box graphic within the graphical mapped area.