Harvest Weed Mapping Using Learning-Based Application Quality Feedback
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Solution Overview
Problem
Agricultural vehicles lack efficient systems for dynamically updating guidance controls based on real-time changes in the agricultural field, leading to inefficient and potentially faulty operations.
Innovation Solution
Implement a vehicle control system with a learning algorithm that receives sensor information, determines the quality of agricultural applications, and adjusts vehicle operations accordingly, using object identification sensors to categorize plants and generate efficacy rates for herbicide or pesticide applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional fixed guidance controls are used in agricultural vehicles, then system simplicity is maintained, but operational efficiency and adaptability to real-time field conditions deteriorate
Solution Approach 1:
The patent implements dynamic guidance controls that continuously adapt to real-time field conditions through sensor data acquisition and machine learning algorithms. The system transitions from static pre-programmed paths to dynamic trajectory adjustments based on actual field conditions, plant locations, and application requirements, thereby improving operational efficiency while managing complexity through modular system architecture.
Solution Approach 2:
The system incorporates continuous feedback loops where sensors monitor field conditions, plant positions, and application quality in real-time. This feedback is processed by machine learning algorithms that adjust guidance controls dynamically, enabling the vehicle to adapt to changing conditions and improve productivity through closed-loop control rather than open-loop pre-programming.
2Adaptability or versatility
If real-time sensor data processing and learning algorithms are implemented, then adaptability to field conditions improves, but device complexity increases
Solution Approach 1:
The patent employs a universal machine learning framework that handles multiple functions including plant identification, application quality assessment, trajectory optimization, and real-time guidance control. This multi-functional algorithm reduces the need for separate specialized systems, thereby improving adaptability while managing overall system complexity through a unified approach.
Solution Approach 2:
The system implements self-service capabilities where the machine learning algorithms automatically learn from sensor data and improve performance without external intervention. The guidance control system self-adjusts based on accumulated data and experience, reducing the need for manual reconfiguration and simplifying operation despite the underlying complexity of the learning algorithms.
3Manufacturing precision
If continuous learning algorithms update vehicle controls, then application accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data and preparing machine learning models before actual application. Trajectories are optimized in advance based on field scans, and application parameters are pre-calculated, allowing real-time execution with minimal processing delay while maintaining high accuracy through pre-computed optimal solutions.
Solution Approach 2:
The learning algorithms update vehicle controls at periodic intervals rather than continuously, processing data in batches or at predetermined frequencies. This periodic updating maintains application accuracy through regular optimization while reducing computational burden and processing time by avoiding constant recalibration, balancing precision with efficiency.
Data Source
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AI summary
A system (200) and a method (600) for controlling an agricultural vehicle (12) includes receiving sensor information for an agricultural vehicle (12) from one or more sensors (120), determining the quality of an agricultural application based on the sensor information, updating a vehicle control learning algorithm for controlling the agricultural vehicle based on the determined quality of the agricultural application, and controlling an operation of the agricultural vehicle based on the updated vehicle control learning algorithm.