Collaborative Field Maps for Avoiding Redundant Planting and Harvesting
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Solution Overview
Problem
Agricultural machines like planters and combines lack effective methods to share and utilize collaborative maps for planting and harvesting operations, leading to inefficiencies such as replanting regions that have already been seeded or harvested, resulting in wasted time and increased operational costs.
Innovation Solution
The implementation of a system that generates and shares collaborative maps between machines in real-time, using geo-referenced data to create seed coverage, harvested coverage, and yield maps, allowing operators to avoid replanting or reharvesting areas that have already been seeded or harvested, through a cloud-based system or direct machine communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machines operate independently without sharing map data, then each machine can complete its operations autonomously, but regions may be replanted or reharvested multiple times causing wasted time and increased operational costs
Solution Approach 1:
The patent merges map data from multiple machines into a shared collaborative map that is accessible to all machines. The system combines individual machine maps (planting maps, harvesting maps, yield maps) into a unified data structure that prevents redundant operations by allowing machines to query and update shared map data in real-time.
Solution Approach 2:
The system implements feedback mechanisms where machines continuously update the shared map with their operational data (planting coverage, harvesting coverage, yield information) and query the map before performing operations. This feedback loop ensures that machines are aware of previously operated regions and can avoid redundant work.
2Productivity
If machines share map data in real-time, then redundant operations are avoided and operational efficiency improves, but system complexity and communication requirements increase
Solution Approach 1:
The patent creates a universal map data structure that serves multiple functions: tracking planting coverage, harvesting coverage, yield calculation, and operational coordination. This multi-functional map reduces the need for separate communication systems for different purposes, thereby managing system complexity while enabling comprehensive machine collaboration.
Solution Approach 2:
The shared collaborative map acts as an intermediary data structure that mediates between multiple machines. Rather than requiring direct machine-to-machine communication, the map serves as a central repository that machines query and update, simplifying the communication architecture while enabling real-time data sharing.
3Measurement precision
If individual machine maps are used without collaboration, then each machine maintains simple independent operation, but yield calculations may be inaccurate when partial header widths are harvested due to previously harvested regions
Solution Approach 1:
The system merges individual machine maps into a shared collaborative map that consolidates all operational information including planting coverage, harvesting coverage, and yield data. This unified map ensures that yield calculations account for previously harvested regions by providing complete information about field operations history.
Solution Approach 2:
The system performs preliminary actions by maintaining an up-to-date shared map of all operational activities before yield calculations are performed. This ensures that when a combine harvests a region, the system already has information about the operation, allowing for accurate yield calculation even when partial header widths are involved.
Data Source
AI summary
Described herein are methods and systems for generating shared collaborative maps for planting or harvesting operations. A method of generating a collaborative shared map between machines includes generating a first map for a first machine based on a first set of data and generating a second map for a second machine based on a second set of data. The method further includes generating at least one shared collaborative map for at least one of the first and second machines based on the first and second maps.