Sprayer Route Planning from Seeder Tracks and Field Conditions
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Solution Overview
Problem
Existing agricultural route planning systems fail to consider complex interactions between terrain, machinery capabilities, soil type, and weather conditions, leading to subpar paths and increased operational costs.
Innovation Solution
A model-based system that generates sprayer and seeder routes using predetermined wayline information, seeder location data, and secondary field information to optimize routes for efficiency and minimize fuel consumption, soil compaction, and crop damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual route planning is used, then operator expertise can be applied, but planning accuracy and route optimization are insufficient
Solution Approach 1:
The patent introduces a computing system as an intermediary between the operator and the route planning process. This system uses machine learning models trained on historical operational data to generate optimized routes, combining the benefits of automated optimization with operator oversight. The intermediary processes complex interactions between terrain, machinery capabilities, and field conditions that would be difficult for manual planning to optimize.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models on historical operational data before actual route planning occurs. This preliminary training enables the system to understand complex field-specific interactions and operational patterns, allowing it to generate optimized routes without requiring real-time complex computations during field operations.
2Adaptability or versatility
If simple heuristic algorithms are used for route planning, then computational simplicity is maintained, but complex interactions between terrain, machinery, soil, and weather are not considered
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance on comprehensive datasets that include terrain, soil type, weather conditions, and machinery capabilities. This pre-computation allows the system to capture complex interactions between these factors during the training phase, so that during actual route planning, the system can efficiently query pre-learned patterns without performing complex real-time simulations.
Solution Approach 2:
The system creates a digital copy or virtual representation of the field environment, including terrain features, soil properties, and historical operational data. This virtual model allows the machine learning algorithm to simulate and understand complex interactions without requiring physical experimentation or complex real-time computations during actual field operations.
3Productivity
If automated computing systems are used, then route optimization improves, but fuel consumption and operational costs increase due to subpar paths
Solution Approach 1:
The patent implements feedback mechanisms where the computing system continuously monitors actual operational data including fuel consumption, operational time, and route effectiveness. This feedback is used to retrain and refine the machine learning models, allowing the system to learn from past performance and progressively improve route optimization to reduce fuel consumption and operational costs while maintaining or enhancing productivity.
Data Source
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AI summary
Technologies for generating sprayer routes based on corresponding seeder routes. In some embodiments, a method includes receiving, by a computing system (102, 200), predetermined sprayer wayline information (106) (step 302). The predetermined sprayer wayline information includes predetermined waylines for a sprayer (110) to be operated in a field. The method also including receiving, by the computing system, seeder location information (104) (step 304). The seeder location information includes locations of a seeder (110a) moving and operating within the field at regular intervals of time. And, the method also including using, by the computing system, the received information as two separate inputs for a model (108) to generate sprayer route information (112) for the field (step 306).