Agricultural Worker Location Data Interpretation
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Solution Overview
Problem
Current methods for interpreting agricultural worker location data are inefficient due to reliance on complex parameters like speed and efficiency, which are prone to error and not suitable for diverse agricultural activities, leading to inaccurate task recognition.
Innovation Solution
A computerized method that uses a database of agricultural activities and exploitation data, combined with temporal zoning data from portable devices, to associate worker location data with specific tasks through a classifier or convolutional neural network, enhancing task recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If fuzzy logic algorithm with complex parameters (speed, efficiency) is used to interpret worker location data, then task recognition can be performed, but the error rate increases and reliability decreases
Solution Approach 1:
The patent changes the parameters used for task recognition from complex human-factor-dependent parameters (speed, efficiency) to simpler, more reliable parameters based on temporal zoning data and pre-defined agricultural activity schedules. This parameter change reduces error rates while maintaining automation capability.
Solution Approach 2:
The system performs preliminary action by pre-defining agricultural activity schedules with multiple tasks and their characteristics before interpretation. This allows the system to match observed temporal zoning data against known patterns, improving recognition reliability without requiring complex real-time analysis of speed and efficiency.
2Extent of automation
If speed and efficiency parameters are used for task recognition, then some level of automation is achieved, but the method becomes unsuitable for diverse agricultural activities
Solution Approach 1:
The patent implements universality by creating a classification system that handles multiple types of agricultural activities through a unified approach. The system uses pre-defined activity schedules and temporal zoning data that can accommodate various agricultural tasks without requiring activity-specific parameter adjustments, making it suitable for diverse agricultural operations.
Solution Approach 2:
The system changes from using activity-specific complex parameters (speed, efficiency) to using universal temporal zoning parameters that work across all agricultural activities. This allows the same automated classification system to handle diverse agricultural tasks reliably.
3Measurement precision
If complex parameters representative of agricultural activity are used, then task recognition can be performed, but the complexity of determining these parameters increases
Solution Approach 1:
The patent extracts the essential information needed for task recognition from complex parameter determination. Instead of calculating speed and efficiency, the system extracts temporal zoning data from portable devices and matches it against pre-defined activity schedules, significantly reducing the complexity of parameter determination while maintaining recognition capability.
Solution Approach 2:
The system performs preliminary action by pre-defining activity schedules with all necessary task characteristics before runtime. This eliminates the need for complex real-time parameter calculation, as the system only needs to match observed temporal patterns against the pre-defined schedules.
Data Source
AI summary
A computerised method for interpreting location data of at least one agricultural worker, wherein: a database of agricultural activities is provided, comprising, for each agricultural activity, a schedule including at least two agricultural tasks, each agricultural task being characterised by a location identifier and a position in the schedule, a farm database is provided, the database comprising at least one parcel of agricultural land characterised by a location identifier and a location, temporal zoning data are provided for the at least one agricultural worker, which data are received from a portable electronic communication device of the agricultural worker, and a computerised interpretation module associates the temporal zoning data with an agricultural task using the temporal zoning data and the agricultural activity and farm databases, the computerised interpretation module associating the temporal zoning data with an agricultural task using the temporal zoning data and the agricultural activity and farm databases.