Predictive Yield Mapping for Harvest Operation Planning and Control
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Solution Overview
Problem
Existing agricultural systems lack accurate predictive yield estimation and efficient operation planning for harvesting operations, leading to inefficiencies such as grain loss and downtime.
Innovation Solution
An agricultural system that utilizes telematic and remote sensing data to generate predictive yield maps, enabling precise control and planning of harvesting operations by integrating processors, sensors, and control systems to optimize machine settings, routes, and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional harvesting operation planning is used, then operation simplicity is maintained, but harvesting efficiency and productivity are reduced due to grain loss and downtime
Solution Approach 1:
The system segments the harvesting operation into multiple components: yield prediction module, operation planning module, and machine control module. Each module processes specific data types (historical yield data, current yield data, worksite data) and generates targeted outputs (predictive yield maps, operation plans, control commands), allowing complex functionality to be managed through modular, independent units that can be developed and maintained separately
Solution Approach 2:
The system performs preliminary actions by generating predictive yield maps before harvesting operations begin. These predictions are created by analyzing historical yield data, current yield data from sensors, and worksite characteristics. The operation plans are formulated in advance based on these predictions, allowing machines to be optimally configured and routes to be predetermined before actual harvesting commences, thereby improving efficiency without proportionally increasing operational complexity
2Measurement precision
If predictive yield estimation is implemented, then operation planning accuracy is improved, but information processing requirements increase
Solution Approach 1:
The system applies local quality by generating predictive yield maps that provide location-specific yield predictions for different areas of the worksite. Rather than using a single average yield value, the system creates spatially-resolved predictions that reflect local variations in soil conditions, topography, and crop characteristics. This allows for precision agriculture practices where harvesting parameters are optimized for each specific location, improving overall prediction accuracy while managing data processing through targeted local analysis
Solution Approach 2:
The system introduces intermediary processing layers that transform raw data from multiple sources (sensors, historical records, weather data) into synthesized predictive yield information. These intermediaries include data fusion algorithms and predictive models that aggregate and reconcile information from various inputs, reducing the raw data processing load while maintaining high prediction accuracy through intelligent data synthesis rather than brute-force processing of all individual data points
3Loss of substance
If harvesting operations are optimized based on predictive yield maps, then grain loss is reduced, but operation planning complexity increases
Solution Approach 1:
The system implements dynamics by creating adaptive operation plans that can be adjusted in real-time based on actual harvesting conditions. The predictive yield maps serve as a dynamic baseline that guides harvesting priorities, but the system continuously monitors actual yield data and adjusts routes, machine settings, and resource allocation accordingly. This dynamic adaptation allows the system to reduce grain loss by responding to actual field conditions while managing planning complexity through automated real-time adjustments rather than static, overly-complex pre-planning
Solution Approach 2:
The system incorporates feedback loops where actual harvesting data from sensors and yield monitors is continuously fed back to the operation planning module. This feedback allows the system to compare predicted versus actual yield, refine predictive models for future operations, and adjust current harvesting strategies to minimize grain loss. The feedback mechanism automates the optimization process, reducing the need for manual intervention and complex human decision-making while systematically reducing substance loss through data-driven adjustments
Data Source
AI summary
An agricultural system includes one or more processors and memory storing instructions, executable by the one or more processors. The instructions, when executed by the one or more processors, cause the one or more processors to perform steps comprising: obtaining historical yield data corresponding to one or more previous harvesting operations at a worksite; obtaining current yield data detected during a current harvesting operation at a worksite; obtaining worksite data indicative of one or more characteristics corresponding to the worksite; generating a predictive yield value corresponding to the worksite based on historical yield data, the current yield data, and the worksite data; generating an operation plan corresponding to an agricultural work machine based on the predictive yield value; and controlling the agricultural work machine based on the operation plan.