Predictive Biomass Map Generation for Harvesting Control
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Solution Overview
Problem
Existing agricultural harvesting systems face challenges in efficiently processing crops due to variations in biomass, leading to suboptimal machine settings and reduced throughput.
Innovation Solution
The system generates a predictive biomass map using in-situ sensors and prior information maps, allowing for real-time adjustment of machine settings to maintain optimal throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine speed is varied to maintain desired throughput, then throughput is maintained, but machine operation complexity increases
Solution Approach 1:
The system uses sensors to detect biomass characteristics in real-time and feeds this information back to the control system, which automatically adjusts machine speed to maintain desired throughput. This closed-loop feedback eliminates the need for manual monitoring and adjustment, reducing operational complexity while maintaining productivity.
Solution Approach 2:
The agricultural machine autonomously monitors its own performance parameters and biomass conditions, making self-adjustments to speed without requiring operator intervention. The system serves itself by automatically optimizing throughput based on real-time sensor data, reducing the complexity of machine operation.
2Ease of operation
If manual monitoring of biomass is performed, then machine settings can be adjusted, but operator workload and errors increase
Solution Approach 1:
The system replaces manual mechanical monitoring with automated electronic sensors and digital processing. Sensors continuously measure biomass characteristics and transmit data to the control system, eliminating the need for visual inspection and manual adjustment by the operator, thereby reducing errors and workload.
Solution Approach 2:
An automated control system acts as an intermediary between the sensor data and machine operation. The control system processes sensor inputs, determines optimal speed adjustments, and executes them automatically, removing the operator from the information processing loop and reducing human errors.
3Measurement precision
If real-time biomass sensing is implemented, then predictive maps can be generated, but device complexity increases
Solution Approach 1:
The sensor system is designed to measure multiple biomass characteristics simultaneously (e.g., biomass concentration, moisture content, plant type) using a single integrated sensor array. This multi-functionality reduces the number of separate devices needed while maintaining high measurement precision for predicting biomass variations.
Solution Approach 2:
The system combines multiple sensor functions and processing operations into a single integrated control system. Sensors, data processing units, and control algorithms are merged into one cohesive system that generates predictive maps, reducing overall device complexity compared to having separate systems for each function.
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.