Harvester Guidance via Crop Attribute Mapping
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Solution Overview
Problem
Current crop harvesting methods lack an efficient and user-friendly way to segregate crops based on attributes, leading to suboptimal market price potential and profitability, as they rely on subjective visual perception and limited automatic sampling systems that fail to accurately determine attribute zones and provide effective guidance for harvesters.
Innovation Solution
The method involves generating site-specific attribute maps using GPS, multi-spectral imaging, and environmental data to guide harvesters in segregating crops by attributes, allowing for multiple batches or loads with accurate mean and variance measurements, and using a mission plan to steer the harvester for efficient crop separation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If crop segregation is based on subjective visual perception by the operator, then the method is simple to implement, but the precision of attribute-based segregation is poor
Solution Approach 1:
The patent replaces the mechanical/subjective visual perception system with an optical sensing system (multi-spectral imaging) and computational processing system. The objective measurement system captures spectral data across multiple wavelengths, processes it through algorithms to estimate crop attributes, and provides precise guidance for segregation, eliminating reliance on human visual judgment.
Solution Approach 2:
The patent introduces an intermediary system consisting of multi-spectral sensors, processing units, and guidance software that mediates between the crop field and the harvester operator. This intermediary objectively measures crop attributes and translates them into actionable guidance, bridging the gap between raw crop variability and precise segregation decisions.
2Measurement precision
If automatic sampling systems are used to estimate attribute levels, then objectivity is improved, but the system lacks effective methods for deciding sampling areas and providing user-friendly guidance
Solution Approach 1:
The patent creates a multi-functional system that simultaneously performs field scanning, attribute estimation, harvest guidance, and batch tracking. The same multi-spectral imaging system and processing platform that estimate attributes also generate the guidance maps and steering paths, eliminating the need for separate systems and simplifying operation.
Solution Approach 2:
The system performs self-service by automatically scanning the field, processing the data to create attribute maps, generating guidance paths, and providing real-time feedback to the operator without requiring manual intervention for each function. The system serves itself by integrating all functions into a unified automated workflow.
3Adaptability or versatility
If the number of segregated batches exceeds the number of storage areas, then crop diversity is captured, but the system complexity increases significantly
Solution Approach 1:
The patent resolves the limitation by adding the time dimension to the storage system. Instead of requiring multiple simultaneous storage areas, the system uses sequential storage and retrieval operations. Batches are stored in available areas and retrieved in the desired sequence, allowing the number of batches to exceed the number of storage areas without increasing physical complexity.
Solution Approach 2:
The system performs preliminary actions by pre-planning the harvest path and batch sequence based on the attribute map before harvesting begins. The mission plan is generated in advance, organizing the sequence of operations so that storage and retrieval operations are coordinated efficiently, reducing the need for complex real-time decision-making.
4Ease of operation
If field topology is used to divide fields into zones, then the method is easy to implement, but it does not accurately correlate with actual crop attribute levels
Solution Approach 1:
The patent changes the parameters used for field division from simple topological parameters (slope, elevation, contour) to spectral parameters (multi-spectral reflectance values). By measuring the actual optical properties of the crop across multiple wavelengths, the system directly correlates field zones with crop attribute levels rather than using indirect topological proxies.
Data Source
AI summary
A method for dividing a field into zones with similar crop attributes and developing a mission plan for steering the harvester to selectively harvest crops based on one or more of the attributes. The attributes include protein level, starch level, oil level, sugar content, moisture level, digestible nutrient level, or any other crop characteristic of interest. The method can be applied to selectively harvest and/or segregate according to attribute any crop, including grains such as wheat, corn, or beans, fruits such as grapes, and forage crops. Directed crop sampling provides absolute value and variance information for segregated batches of harvested crop.