Crop Row Vision Navigation for Precise Agricultural Steering
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Solution Overview
Problem
Existing agricultural navigation systems, including GPS-based and image-based methods, struggle to accurately and robustly control agricultural machines to avoid crop damage by ensuring they stay between crop rows, particularly due to errors in GPS data and mechanical deficiencies.
Innovation Solution
Employing a convolutional neural network in a vision-based system using a depth camera and trained crop row detection models to generate navigational parameters for steering control, refining GPS data with real-time image analysis to maintain the vehicle within crop rows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If GPS-based navigation systems are used to locate vehicle position and guide agricultural equipment through fields, then navigation capability is provided, but positioning accuracy deteriorates due to satellite signal errors and mechanical deficiencies
Solution Approach 1:
The patent combines GPS-based navigation with vision-based navigation systems. The GPS provides coarse navigation capability while the vision system using cameras and image processing provides precise positioning by detecting crop rows and vehicle position relative to them, thereby compensating for GPS accuracy limitations
Solution Approach 2:
The patent introduces an intermediary correction mechanism where a base station with known coordinates calculates GPS errors and provides correction data to mobile receivers. This intermediary system mediates between the imperfect GPS signals and the required accurate positioning, correcting satellite signal errors through differential GPS technology
2Measurement precision
If image-based control systems are used to guide agricultural equipment through crop rows, then positioning accuracy is improved, but system complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The vision-based system serves multiple functions: it detects crop row positions, determines vehicle location between rows, identifies terrain features, and provides navigation guidance. By using a single camera system for multiple purposes, the patent reduces overall system complexity compared to having separate systems for each function
Solution Approach 2:
The system uses the agricultural vehicle's own camera to capture images of the environment, and the onboard processor analyzes these images to determine position and guide the vehicle. The system is self-contained, using its own resources (camera, processor) rather than requiring external infrastructure, thereby reducing complexity
3Ease of operation
If manual steering is used by human operators to keep vehicle between crop rows, then navigation control is achieved, but operator fatigue increases due to substantial concentration and physical control requirements over extended periods
Solution Approach 1:
The patent replaces manual mechanical steering control with an automated control system. The vision-based navigation system automatically processes images, calculates vehicle position, and controls steering mechanisms without human intervention, eliminating operator fatigue while maintaining navigation control
Solution Approach 2:
The system enables the agricultural vehicle to steer itself automatically. The onboard computer processes visual information and autonomously controls the steering mechanism, making the vehicle self-navigating without requiring continuous human operation and monitoring
Data Source
AI summary
An agricultural machine includes a vehicle, an image detector, memory, a processing circuit, and a steering control unit. The vehicle has at least one agricultural implement affixed thereto. The image detector is mounted to the vehicle and is configured to generate first image data. The memory stores at least a first trained model of a crop row detection neural network. The processing circuit is configured to generate a crop row prediction image using the first image data, the first trained model, and a convolutional neural network. The processing circuit is further configured to generate navigational parameters based at least in part on the crop row prediction. The steering control unit is configured to control movement of vehicle based on the navigational parameters.