Agricultural Implement Anomaly Detection With Multi-Sensor AI Control
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Solution Overview
Problem
Existing anomaly detection systems for agricultural machines, particularly those using LiDAR, GNSS, and radar, struggle with accuracy and complexity in detecting small or irregularly shaped obstacles in agricultural settings, and camera-based systems face issues with varying lighting conditions and occlusions, failing to consider spatial distributions and relationships between anomalies.
Innovation Solution
An agricultural machine equipped with spatial sensors and implement sensors uses deep neural networks to generate anomaly predictions, allowing autonomous control of operations based on these predictions, including stopping, slowing down, or altering the route to avoid detected anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LiDAR, GNSS, and radar units are used for anomaly detection, then detection coverage is improved, but system complexity and cost increase
Solution Approach 1:
The patent combines multiple sensor types (LiDAR, GNSS, radar, cameras) into a unified anomaly detection system that processes data from all sensors through a common deep learning model, allowing the system to leverage the strengths of each sensor while reducing overall complexity through integrated processing
Solution Approach 2:
The deep learning anomaly detection model serves multiple functions by processing data from different sensor types and detecting various kinds of anomalies (small objects, thin obstacles, irregularly shaped features) with a single unified approach, reducing the need for separate detection systems for each sensor type
2Device complexity
If camera-based anomaly detectors are used, then cost is reduced and visual information is improved, but performance deteriorates under varying lighting conditions and occlusions
Solution Approach 1:
The system merges camera-based detection with other sensor types (LiDAR, radar) and processes all data through a unified deep learning model, allowing the camera to provide cost-effective visual information while being compensated by data from other sensors to maintain accuracy under varying lighting and occlusion conditions
Solution Approach 2:
The deep learning model dynamically adjusts its processing parameters based on the quality and characteristics of input data from different sensors, adapting to varying lighting conditions and occlusions by weighting and processing camera data in combination with data from other sensor types
3Device complexity
If traditional image processing techniques are used, then system complexity is reduced, but detection accuracy for small and irregular objects deteriorates
Solution Approach 1:
The patent replaces traditional mechanical image processing techniques with a deep learning neural network that automatically learns features from sensor data, enabling high-precision detection of small and irregularly shaped objects without requiring complex manual feature engineering or processing algorithms
4Speed
If anomaly detection systems do not consider spatial distributions, then processing speed is improved, but detection accuracy for moving anomalies deteriorates
Solution Approach 1:
The system performs preliminary spatial analysis by integrating data from multiple sensors that capture spatial information (LiDAR for depth, GNSS for position, radar for velocity) before anomaly detection, pre-processing the spatial relationships to enable faster subsequent processing while maintaining accurate detection of moving anomalies
Data Source
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AI summary
An agricultural machine includes an agricultural machine and an agricultural implement coupled to the agricultural machine, one or more spatial sensors coupled to the agricultural vehicle, one or more agricultural implement sensors coupled to the agricultural implement, and an anomaly detection system that receives spatial data from the one or more spatial sensors and agricultural implement sensor data from the agricultural implement sensors. The anomaly detection system operates on a computing device including at least one processor, and instructions that cause the processor to receive the spatial data and the agricultural implement sensor data, utilize advanced machine learning model techniques to detect anomaly predictions associated with the agricultural implement based on the spatial data and the agricultural implement sensor data and control operations of the agricultural implement based on the anomaly predictions of the associated with the agricultural implement. Related agricultural machines and methods are also disclosed.