Agricultural Implement Anomaly Detection With Sensor Fusion Control
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Solution Overview
Problem
Existing anomaly detection systems for agricultural machines, particularly those incorporating camera-based features, struggle with accuracy, cost, and complexity in detecting anomalies in complex agricultural environments, especially small objects and irregularly shaped features, and fail to consider spatial distributions and relationships between anomalies, especially when the vehicle and anomalies are moving.
Innovation Solution
An agricultural machine equipped with spatial sensors and implement sensors uses deep neural networks to generate anomaly predictions, allowing for autonomous detection and control of agricultural implements based on these predictions, including cameras, LiDAR units, and additional sensors like IMUs and hydraulic sensors, to monitor and control operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR units and radar units are used for anomaly detection, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple sensor types (cameras, LiDAR, radar) into a unified anomaly detection system that processes data from all sources simultaneously. This merging approach allows the system to leverage the strengths of each sensor type while sharing processing infrastructure, thereby improving measurement precision without proportionally increasing device complexity
Solution Approach 2:
The anomaly detection system is designed to perform multiple functions using a single integrated platform: it can detect various types of anomalies (stones, non-standard structures, misplaced objects, power poles, trees, buildings, bodies of water, human bystanders, animals) and works with different sensor inputs (camera images, LiDAR point clouds, radar data). This multi-functionality reduces the need for separate specialized systems for each anomaly type
2Device complexity
If camera-based anomaly detectors are used, then cost is reduced, but measurement precision deteriorates due to difficulty in handling varying lighting conditions, occlusions, and clutter
Solution Approach 1:
The system merges camera-based detection with LiDAR and radar data processing. The camera provides cost-effective visual information while LiDAR and radar compensate for lighting conditions, occlusions, and clutter issues. By combining these sensor types, the system maintains low cost characteristics while improving measurement precision beyond what cameras alone can achieve
Solution Approach 2:
The patent uses LiDAR and radar data as intermediary information to enhance camera-based detection. When camera images suffer from poor lighting or occlusions, the LiDAR point clouds and radar returns serve as intermediary data sources that provide complementary information about the environment, allowing the system to maintain accurate anomaly detection without relying solely on camera data
3Device complexity
If traditional image processing techniques are used, then device complexity is reduced, but measurement precision deteriorates in detecting small objects, thin obstacles, or irregularly shaped features
Solution Approach 1:
The patent replaces traditional mechanical image processing techniques with deep learning-based neural networks. These neural networks are specifically trained to detect small objects, thin obstacles, and irregularly shaped features by learning complex patterns from training data. This substitution maintains relative processing simplicity through automated feature extraction while dramatically improving measurement precision for challenging anomaly types
4Device complexity
If anomaly detection systems do not consider spatial distributions, then device complexity is reduced, but measurement precision deteriorates when anomalies are moving or when the vehicle is moving
Solution Approach 1:
The anomaly detection system incorporates dynamic spatial analysis that tracks and considers the movement of both the agricultural vehicle and detected anomalies over time. The system processes sequential data from multiple time points, updating anomaly positions and predicting trajectories. This dynamic approach maintains reasonable processing complexity while significantly improving measurement precision for moving anomalies by leveraging temporal information and spatial relationships across multiple observations
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 efficiency and safety of agricultural operations by autonomously detecting and responding to anomalies, reducing the need for manual monitoring and minimizing damage to the machine and implement.
Implementation Method 1
receiving spatial data of at least one of the agricultural implement or the agricultural field worked by the agricultural implement from one or more spatial sensors coupled to the agricultural vehicle or one or more LiDAR units coupled to the agricultural vehicle
Implementation Method 2
one or more LiDAR units coupled to the agricultural vehicle
Data Source
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.