Autonomous Tillage Plug Detection With Machine-Learned Sensor Monitoring
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Solution Overview
Problem
Existing autonomous farming systems lack effective real-time monitoring and detection capabilities for malfunctions in tilling assemblies, leading to reduced tilling quality and potential equipment damage due to detached components or plugs, especially under varying environmental conditions.
Innovation Solution
A detection system utilizing machine-learned models and sensors, such as cameras and thermal sensors, to continuously monitor the tilling assembly for malfunctions, including loose or missing shanks and plugged tilling shanks, providing real-time data analysis and recommendations for resolving issues during autonomous operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring of tilling assembly is used, then the system complexity is low, but the detection rate and accuracy of malfunctions is reduced
Solution Approach 1:
The patent replaces manual monitoring with an automated detection system that uses sensors (cameras, thermal sensors, ultrasonic sensors) and machine-learned models to detect malfunctions. This substitution of mechanical/manual inspection with automated sensing and computational analysis directly resolves the contradiction by providing high detection accuracy while maintaining manageable system complexity through modular sensor integration.
Solution Approach 2:
The detection system operates autonomously to monitor the tilling assembly, detecting malfunctions without requiring continuous manual intervention. The system uses self-contained sensors and onboard processing to perform detection, classification, and reporting functions, thereby achieving high detection accuracy while reducing the operational complexity associated with manual monitoring protocols.
2Productivity
If manual monitoring of tilling assembly is used, then the device complexity is low, but the productivity and timely detection of malfunctions is reduced
Solution Approach 1:
The detection system operates continuously during tilling operations, with sensors constantly monitoring the tilling assembly for malfunctions. The system maintains uninterrupted detection capability through continuous data acquisition from multiple sensors and real-time processing, achieving high detection rates while managing complexity through continuous operational monitoring rather than intermittent manual checks.
Solution Approach 2:
The patent replaces manual inspection processes with automated sensing systems that operate at high speeds, enabling rapid detection of malfunctions. The use of cameras, thermal sensors, and machine-learned models provides continuous, high-rate detection capability that significantly improves productivity compared to manual monitoring, while the modular architecture keeps system complexity manageable.
3Measurement precision
If detection system with multiple sensors is used, then the detection accuracy is improved, but the use of energy and device complexity increase
Solution Approach 1:
The detection system is divided into multiple independent sensor modules (camera module, thermal sensor module, ultrasonic sensor module), each performing specific detection functions. This segmentation allows the system to achieve high detection accuracy through multiple specialized sensors while managing energy consumption by enabling selective activation of sensor modules based on operational conditions and malfunction types being monitored.
Solution Approach 2:
The system employs multiple sensors providing redundant detection capabilities, where not all sensors need to operate at full capacity simultaneously. The machine-learned models process data from multiple sources, allowing the system to achieve high detection accuracy while managing energy consumption by optimizing sensor activation and data processing intensity based on operational context.
4Productivity
If real-time monitoring is implemented, then the detection rate is improved, but the loss of time for data processing and the complexity of real-time analysis increases
Solution Approach 1:
The system performs preliminary processing of sensor data through optimized data acquisition and pre-processing pipelines before full analysis. Machine-learned models are pre-trained and deployed for rapid inference, allowing the system to achieve real-time detection rates while minimizing data processing time through advance preparation of detection algorithms and data processing routines.
Solution Approach 2:
The patent replaces manual data analysis with automated machine-learned models that process sensor data in real-time. These computational models rapidly classify detected objects and determine malfunction conditions, achieving high detection rates while minimizing processing time compared to manual analysis. The automated decision-making process eliminates the time loss associated with human interpretation of sensor data.
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 the detection rate and accuracy of malfunctions, allowing for timely intervention and improving the overall quality of autonomous farming routines by identifying and addressing issues promptly, even under challenging environmental conditions.
Implementation Method 1
a monochrome camera captures light reflected off of reflective markers, each marker coupled to a tilling shank
Implementation Method 2
a thermal camera captures thermal radiation from below-ground tilling sweeps
Data Source
AI summary
A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).