Agricultural Operation Mapping Using Machine Vision Tracking
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Solution Overview
Problem
Existing agricultural operation monitoring systems are inadequate for coordinating multiple machines in a working environment, especially when older machinery or towed implements lack GNSS and communication capabilities, making it difficult to track and manage operations in varying conditions.
Innovation Solution
A system utilizing imaging sensors mounted on agricultural machines to obtain image data, which is analyzed using a detection model to classify objects and identify working machines, logging operational information into an operational map, even for machines without positioning or communication systems, providing redundancy and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a fully connected system with GNSS and data networks is used to monitor and track machines, then operational coordination and tracking capability are improved, but device complexity increases and reliability decreases for older machinery without such functionality
Solution Approach 1:
The imaging sensor system serves multiple functions: it captures images for operational mapping, identifies machines through image analysis, and provides tracking capability universally across all machine types regardless of their native equipment. This multi-functional approach eliminates the need for separate tracking systems on each machine.
Solution Approach 2:
The system introduces an intermediary imaging sensor system that mediates between the need for tracking and the limitation of older machinery. Instead of requiring machines to have built-in tracking capabilities, the imaging system acts as an external mediator that captures and processes visual data to achieve tracking functionality.
2Reliability
If a fully connected system with stable communication links is implemented, then operational monitoring reliability is improved, but adaptability to varying operating conditions and older machinery decreases
Solution Approach 1:
The imaging sensor system is self-sufficient and does not require external communication infrastructure or stable data links. It independently captures images, processes them through image analysis, and generates operational maps without needing continuous communication with remote servers or other machines, making it adaptable to all operating conditions.
Solution Approach 2:
The system uses standard imaging sensors that are widely available and can be deployed on any machine without requiring expensive specialized communication equipment. This approach prioritizes widespread compatibility over high-end communication reliability, accepting that the system works with simpler, more universally applicable components.
3Adaptability or versatility
If imaging sensors are used to identify machines without positioning systems, then adaptability to older machinery is improved, but measurement precision of machine positions may be affected
Solution Approach 1:
The system replaces mechanical positioning systems (GNSS, encoders) with an optical imaging system. Instead of using mechanical sensors on each machine to determine position, the system uses imaging sensors to capture visual data and derives position information through image analysis, substituting mechanical measurement with optical observation.
Solution Approach 2:
The imaging system creates a visual copy or representation of the physical environment and machines within it. By capturing images and analyzing them, the system creates a digital replica of machine positions and operations, allowing tracking without direct mechanical measurement from the machines themselves.
Data Source
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AI summary
Systems and methods are provided for mapping one or more agricultural operations within a working environment. Image data is received (202) from one or more imaging sensors (29) mounted or otherwise associated with one or more agricultural machines (10, MOa, MOb) within the working environment and configured to obtain image data indicative of the working environment and/or one or more objects located therein. The data is analysed (204) utilising a detection model to classify one or more objects within the working environment to identify (206), from the one or more classified objects, one or more working machines within the working environment. Operational information for the working machine(s) is logged and/or updated (208) in an operational map of the working environment in dependence on the identification of the classified object(s).