Bale Weight Control Using Prediction Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Agricultural balers face challenges in maintaining consistent bale weights due to variations in drive speed, crop density, and crop properties, leading to adjustments based on outdated data and infrequent parameter adjustments, resulting in inaccurate and delayed control of bale weight.
Innovation Solution
A method that utilizes a weight prediction model to adjust baling parameters in real-time during bale formation, incorporating crop and baler operating data to generate a predicted final weight and adjust setpoints accordingly, allowing for more frequent and accurate control of bale weight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If parameter adjustments are made based on measured bale weight, then bale weight consistency can be improved, but the control is delayed because adjustments are based on outdated data from previous bales
Solution Approach 1:
The system performs preliminary actions by predicting the final bale weight during bale formation using real-time sensor data and a prediction model, allowing parameter adjustments to be made proactively before the bale is complete rather than reactively after weighing. This eliminates the delay inherent in waiting for bale completion and weighing before making adjustments.
Solution Approach 2:
The system implements continuous feedback by constantly monitoring bale formation parameters (crop density, moisture, compression force) and using a prediction model to estimate final bale weight in real-time. This feedback loop enables dynamic parameter adjustments during bale formation to maintain target weight consistency, rather than waiting for post-formation weight measurements.
2Productivity
If parameter adjustments are made once per bale based on measured weight, then system complexity is reduced, but the frequency of adjustments is insufficient to account for rapid changes in crop conditions
Solution Approach 1:
The system uses continuous feedback from multiple sensors monitoring crop parameters (moisture, density, flow rate) and bale formation parameters during the entire bale formation process. This real-time feedback enables frequent parameter adjustments without requiring complex manual intervention, as the system automatically processes sensor data and adjusts parameters dynamically.
Solution Approach 2:
The system replaces manual parameter adjustment mechanisms with an automated electronic control system that uses prediction models and sensor data to dynamically adjust bale formation parameters. This substitution of mechanical/manual adjustment with automated electronic control increases adjustment frequency while managing system complexity through software-based solutions.
3Measurement precision
If real-time prediction and adjustment systems are implemented, then bale weight control accuracy is improved, but device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The system replaces physical weighing of completed bales with a virtual prediction model that calculates expected bale weight in real-time based on sensor data from the bale formation process. This substitution eliminates the need for complex post-formation weighing infrastructure while providing continuous weight predictions, reducing overall system complexity despite adding prediction algorithms.
Solution Approach 2:
The prediction model acts as an intermediary between raw sensor data and bale weight control decisions. Rather than directly measuring final bale weight, the system uses the prediction model to translate real-time sensor measurements (crop parameters, compression forces) into weight predictions, enabling accurate weight control without direct weight measurement during formation.
Data Source
Figure 1
Figure 2
AI summary
The application relates to a method of controlling the weight of bales made by an agricultural baler (10). The method comprises: receiving a bale weight setpoint; receiving crop parameter data; and, while a bale is being made: receiving baler operating data relating to baling parameters; inputting the crop parameter data and baler operating data into a weight prediction model to generate a predicted final weight of the bale; and changing a baling parameter setpoint of one of the baling parameters based on the predicted final weight of the bale and the bale weight setpoint.