Predictive Field Maps for Harvester Calibration Control
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Solution Overview
Problem
Agricultural harvesters face challenges in accurately adjusting their operations due to varying field conditions, such as different crop genotypes, vegetative indices, and moisture levels, leading to inaccuracies in yield and grain loss sensing.
Innovation Solution
The system generates predictive maps using in-situ sensors and historical data to model relationships between agricultural characteristics, enabling precise control of harvester operations by predicting calibration characteristics across the field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensing methods are used without predictive modeling, then the system is simpler and faster to operate, but measurement precision of yield and grain loss deteriorates due to varying field conditions
Solution Approach 1:
The system performs preliminary actions by collecting historical sensor data and generating predictive maps before the actual harvesting operation. These predictive maps pre-establish the relationship between sensor readings and actual yield/grain loss values for different field conditions, so when harvesting occurs, the system can quickly apply the appropriate calibration without real-time complex calculations, thus improving measurement precision without proportionally increasing operational complexity
Solution Approach 2:
The system changes parameters by using multiple sensor types (optical, electromagnetic, acoustic) that measure different physical parameters of the crop (vegetative index, moisture content, density). By combining these different parameter measurements with historical data, the system creates a multi-dimensional predictive model that adapts to varying field conditions, thereby improving yield and grain loss sensing accuracy
2Measurement precision
If multiple sensor types and historical data are integrated, then measurement precision improves, but the time required for data processing and map generation increases
Solution Approach 1:
The system performs data processing in advance by generating predictive maps during previous field operations or off-season periods. Historical sensor data from multiple passes are processed beforehand to establish calibration relationships, so during actual harvesting, only straightforward data lookup and application are needed, significantly reducing real-time processing time while maintaining high measurement precision
Solution Approach 2:
The system creates simplified copies or representations of complex field conditions through predictive maps. Instead of processing raw multi-sensor data in real-time, the system creates pre-processed data structures (predictive maps) that capture the essential relationships between sensor readings and actual agricultural characteristics, enabling fast querying during harvesting without re-processing the original complex datasets
3Area of stationary object
If predictive maps are generated for the entire field, then coverage area increases, but the complexity of data management and processing increases
Solution Approach 1:
The system segments the field into multiple zones or regions, each with its own predictive map or calibration parameters. Instead of managing one massive dataset for the entire field, the system divides the area into manageable segments that can be processed and stored independently. This segmentation reduces the complexity of data management while still providing comprehensive coverage, as each segment can be handled with appropriate computational resources
Solution Approach 2:
The system applies local quality by creating zone-specific predictive maps that are tailored to the unique characteristics of each field region. Rather than using a single uniform model for the entire field, each zone has customized calibration parameters based on its specific conditions (soil type, crop variety, historical performance). This approach manages data complexity by focusing processing power on local characteristics rather than attempting to model the entire field uniformly
Data Source
AI summary
One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.