Ground-Engaging Tool Plugging Detection via Machine Learning
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Solution Overview
Problem
Current systems for detecting plugging of ground-engaging tools in agricultural implements lack accuracy, leading to poor seedbed quality and inefficient operations during tillage operations.
Innovation Solution
A computing system utilizing a machine-learned classification model processes image data from cameras or LIDAR sensors to determine soil flow characteristics around ground-engaging tools, classifying them as plugged or non-plugged, and adjusts operational parameters to prevent or resolve plugging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based systems are used to detect plugging of ground-engaging tools, then the system complexity is reduced, but the measurement precision and detection accuracy deteriorate
Solution Approach 1:
The patent replaces traditional mechanical sensors with a machine-learned classification model that processes imagery data. The imaging device captures visual information of soil flow around ground-engaging tools, and the machine-learned model classifies this data to detect plugging conditions. This substitution of mechanical detection with optical imaging and computational analysis resolves the contradiction by achieving higher measurement precision through advanced image processing while managing system complexity through software-based solutions.
Solution Approach 2:
The patent introduces imagery data as an intermediary between the ground-engaging tools and the detection system. Instead of directly sensing mechanical properties of plugging, the system captures images of soil flow patterns and uses machine learning to infer plugging status. This intermediary approach enables more accurate detection by analyzing visual characteristics of soil movement, resolving the accuracy-complexity tradeoff.
2Measurement precision
If machine-learned classification models are used to detect plugging, then the measurement precision improves, but the use of energy and computational resources increases
Solution Approach 1:
The patent processes imagery data through a machine-learned classification model that analyzes visual features of soil flow. By focusing computational resources on key discriminative features in the images rather than processing all possible data, the system achieves high detection accuracy while managing energy consumption. The model processes only the necessary visual information required for plugging detection, applying partial action principle to optimize the energy-accuracy tradeoff.
3Productivity
If real-time imagery processing is performed to detect plugging, then the productivity and response time improve, but the device complexity and processing requirements increase
Solution Approach 1:
The patent replaces complex mechanical monitoring systems with an imaging-based detection system using machine learning. The imaging device and computational model provide real-time plugging detection without the mechanical complexity of traditional sensor arrays and processing systems. This substitution enables productivity improvement through faster, more reliable detection while reducing overall system complexity by using software-based analysis instead of complex hardware.
Data Source
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AI summary
A computing system (100, 116) may be configured to perform operations including obtaining image data depicting a flow of soil around a ground-engaging tool (36, 38, 40) of an agricultural implement (10) as the ground-engaging tool (36, 38, 40) is moved through the soil. Furthermore, the operations may include extracting a set of features from the obtained image data. Moreover, the operations may include inputting the set of features into the machine-learned classification model (108, 150) and receiving a soil flow classification of the set of features as an output of the machine-learned classification model (108, 150). In addition, the operations may include determining when the ground-engaging tool (36, 38, 40) is plugged based on the soil flow classification of the set of features.