Crop Transfer Control Using Ballistics-Based Engine Speed
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Solution Overview
Problem
Simultaneously controlling the harvesting and crop transfer process in agricultural harvesters to prevent crop spillage is challenging, even for experienced operators, and existing camera-based systems have limitations in achieving full automation with zero loss.
Innovation Solution
A method and system that predict crop ballistics by monitoring trailer and crop cloud parameters over time, determining a minimum engine speed to ensure successful transfer, using sensors like LIDAR and machine learning for accurate control, and adjusting the flipper angle to optimize the crop delivery process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If manual or semi-automatic control methods are used for crop transfer, then operator experience can help manage the process, but crop spillage still occurs and driver comfort is reduced
Solution Approach 1:
The system enables self-service automation where the crop transfer process is controlled automatically without continuous driver intervention. The control system autonomously manages engine speed, flipper angle, and crop flow based on sensor data and predictive algorithms, freeing the driver from manual control tasks while ensuring precise crop delivery to eliminate spillage.
2Reliability
If high engine speed is maintained to ensure crop reaches the trailer, then crop transfer reliability is improved, but fuel economy deteriorates
Solution Approach 1:
The system dynamically adjusts engine speed based on real-time conditions rather than maintaining a constant high speed. The control system continuously monitors crop flow characteristics, trailer position, and environmental factors, adjusting engine speed to the minimum required level to ensure reliable crop delivery. This dynamic optimization maintains crop transfer reliability while minimizing fuel consumption.
Solution Approach 2:
The system changes operational parameters (engine speed, flipper angle) based on predicted crop ballistics and actual transfer conditions. By adjusting these parameters dynamically according to calculated trajectories and real-time feedback, the system ensures reliable crop delivery at the lowest necessary engine speed, optimizing the balance between reliability and fuel economy.
3Extent of automation
If camera-based automatic control systems are used, then some automation is achieved, but full automation with zero crop loss is not yet realized
Solution Approach 1:
The system replaces camera-based optical detection with direct physical sensing using sensors that measure crop flow characteristics, velocity, and trajectory. This substitution provides more accurate and reliable data for controlling crop ballistics, enabling full automation to achieve zero crop loss by directly measuring and controlling the physical parameters of crop movement rather than relying on visual estimation.
Solution Approach 2:
The system implements continuous feedback control by monitoring crop flow parameters in real-time and adjusting engine speed and flipper angle accordingly. Sensors provide feedback on crop velocity, trajectory, and delivery accuracy, which the control system uses to make real-time adjustments, ensuring zero crop spillage through closed-loop automation rather than open-loop camera-based control.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances driver comfort, improves fuel economy, and reduces crop spillage by optimizing engine speed and flipper angle for precise crop transfer, achieving better automation than previous methods.
Implementation Method 1
using sensors like LIDAR and machine learning for accurate control
Data Source
AI summary
A method of controlling a crop transfer process for transferring crop between an agricultural harvester and a nearby trailer. The method includes determining at least one trailer status parameter at a plurality of different points in time; determining at least one crop cloud parameter at the plurality of different points in time; determining a target point in the trailer for the crop; determining a minimum engine speed of the harvester based on the determined at least one trailer status parameter and at least one crop cloud parameter for the crop to be transferred to the target point; and changing the engine speed of the harvester to the determined minimum engine speed.