Predictive Map Generation for Agricultural Harvesting
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Solution Overview
Problem
Agricultural machines face challenges in optimizing operations due to varying agricultural characteristics across fields, as existing technologies lack effective methods to predict and adapt to these changes in real-time, leading to inefficiencies in harvesting and resource management.
Innovation Solution
The use of in-situ sensors on agricultural machines to collect real-time data, combined with prior data from seeding maps, generates predictive maps that forecast agricultural characteristics, enabling optimized machine control and operation adjustments such as header position and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time sensing and predictive mapping are implemented, then harvesting efficiency and adaptability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by generating predictive maps before harvesting operations begin. Seeding characteristic maps are created in advance, and predictive models are trained beforehand to forecast agricultural characteristics during harvesting, allowing the system to adapt in real-time without complex on-the-fly computations
Solution Approach 2:
The patent introduces intermediary elements including predictive models that act as mediators between seeding data and harvesting operations, and control systems that serve as intermediaries between predictive maps and machine adjustments. These intermediaries simplify the direct complexity by breaking down the problem into manageable computational stages
2Adaptability or versatility
If predictive mapping based on seeding characteristics is used, then adaptability to field variations is improved, but measurement precision requirements increase
Solution Approach 1:
The system applies local quality by creating spatially-resolved predictive maps that capture local variations in agricultural characteristics across different field zones. Each location receives customized predictions based on its specific seeding characteristics and environmental conditions, allowing targeted adaptations rather than uniform field-wide adjustments
Solution Approach 2:
Seeding characteristic maps are created in advance with high precision measurements of planting data. This preliminary high-precision mapping provides a detailed foundation that reduces the need for equally precise real-time measurements during harvesting, as the predictive model can interpolate and predict characteristics in areas where direct measurements are less precise
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.