Harvester Control Automation Yield Quality Balance

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Solution Overview

Problem

Modern agricultural harvesters face challenges in optimizing crop harvesting parameters to simultaneously achieve maximum yield and quality, as adjusting one parameter often compromises the other, leading to complex decision-making processes for operators, especially when the harvested crop is intended for specific end products like milk, meat, or biogas.

Innovation Solution

A method and system for controlling agricultural harvesters that receive harvesting data to calculate current yield and quality parameters, allowing for adaptive adjustment of operational parameters such as drive speed, header height, and kernel processor settings based on models predicting end product output, enabling prioritization of yield, quality, or fuel efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If the user adjusts operational parameters to increase the amount of crop harvested, then the yield increases, but the fuel consumption increases

Engineering Contradiction:
Improveamount of crop harvestedVSAvoidfuel consumption
Core Design Contradiction:
ProductivityVSUse of energy by moving object

Solution Approach 1:

The system dynamically changes operational parameters (forward speed, header height, feed roll speed, kernel processor settings) based on real-time sensor data and predictive models to optimize the balance between crop harvested and fuel consumption. The controller continuously adjusts these parameters to achieve optimal harvesting efficiency while minimizing energy use.

Inventive Principle:
Principle #35Parameter changes

2Manufacturing precision

If the user raises the header to improve the nutritional value of the harvested crop, then the quality increases, but the total volume of harvested material decreases

Engineering Contradiction:
Improvenutritional value of harvested cropVSAvoidtotal volume of harvested material
Core Design Contradiction:
Manufacturing precisionVSQuantity of substance

Solution Approach 1:

The system dynamically adjusts the header height parameter based on real-time sensor data and predictive models to optimize the balance between nutritional value and total volume harvested. The controller continuously modifies this parameter to achieve the desired quality-quantity trade-off.

Inventive Principle:
Principle #35Parameter changes

3Productivity

If the user takes in more crop and cuts it into smaller pieces, then the yield increases, but the fuel consumption increases

Engineering Contradiction:
ImproveyieldVSAvoidfuel consumption
Core Design Contradiction:
ProductivityVSUse of energy by moving object

Solution Approach 1:

The system dynamically adjusts operational parameters including feed roll speed and cutterhead speed to optimize the balance between yield and fuel consumption. The controller continuously modifies these parameters based on real-time sensor data and predictive models to achieve optimal harvesting efficiency while minimizing energy use.

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentEP4245111A1Improved harvester control automation
Publication Date: 2023.09.20 CNH IND BELGIUM NV
  • EP4245111A1 patent drawingFigure 1
  • EP4245111A1 patent drawingFigure 2
  • EP4245111A1 patent drawingFigure 3~4

AI summary

A method is provided for controlling an agricultural harvester (10) during harvesting. The method comprises the steps of receiving harvesting data, calculating a current yield parameter and a current quality parameter, and controlling at least one operational parameter of the agricultural harvester (10) in dependence of the current yield parameter and/or the current quality parameter. The harvesting data indicates at least an amount of harvested crop (500) and a nutritional attribute of the harvested crop (500). The current yield parameter represents an amount of end product per unit of harvested area. The current quality parameter represents an amount of end product per amount of harvested crop (500). The yield and quality parameters are calculated based on the received harvesting data and using a model for predicting an amount of end product that can be produced with the harvested crop (500).