Adaptive Vision Sensor Control for Unpredictable Field Terrain
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Solution Overview
Problem
Agricultural sensors face challenges in capturing high-quality and consistent images in unpredictable terrain, such as agricultural fields with varying crops and terrain conditions, leading to inaccurate inferences due to environmental and human-induced factors.
Innovation Solution
Implementing adaptive adjustments to vision sensors and vehicle operations using edge computing devices, which process images with machine learning models to determine quality metrics and trigger adjustments in sensor parameters or vehicle operations, such as interaxial distance, framerate, aperture size, and vehicle speed, to improve image quality and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If vision sensors are mounted on vehicles to capture images in agricultural fields, then sensor data can be gathered for agricultural inferences, but image quality and consistency deteriorate due to unpredictable terrain and environmental factors
Solution Approach 1:
The system dynamically adjusts vision sensor parameters (focal length, exposure time, gain) and vehicle operating parameters (speed, position) in real-time based on terrain conditions and plant characteristics detected by the machine learning model, transforming static sensor configurations into adaptive systems that maintain image quality across varying terrains
Solution Approach 2:
The patent changes multiple parameters simultaneously including sensor focal length, exposure time, gain settings, and vehicle speed to optimize image capture quality for different terrain conditions and plant features, using machine learning-driven decisions to coordinate these parameter adjustments
2Measurement precision
If sensor parameters are fixed to simplify operation, then ease of operation improves, but measurement precision deteriorates in unpredictable environments
Solution Approach 1:
The vision sensor system performs self-adjustment through automated machine learning models that analyze terrain and plant characteristics, then autonomously configure optimal sensor parameters without requiring manual intervention, making the complex system as easy to operate as fixed parameters while achieving superior measurement precision
Solution Approach 2:
The system uses machine learning models to continuously analyze captured images and terrain data, then feeds this information back to adjust sensor parameters and vehicle operation in real-time, creating a closed-loop control system that maintains high inference accuracy automatically
3Stability of the object's composition
If multiple sensor adjustments are made to improve image quality, then image consistency improves, but device complexity increases
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: analyzing terrain conditions, identifying plant characteristics, determining optimal sensor parameters, and controlling vehicle operation, consolidating what would otherwise require multiple separate systems into a single multi-functional platform that improves image consistency without proportionally increasing complexity
Data Source
AI summary
Implementations are disclosed for adaptively adjusting various parameters of equipment in unpredictable terrain, such as agricultural fields. In various implementations, edge computing device(s) may obtain a first image captured by vision sensor(s) transported across an agricultural field by a vehicle. The first image may depict plant(s) growing in the agricultural area. The edge computing device(s) may process the first image based on a machine learning model to generate agricultural inference(s) about the plant(s) growing in the agricultural area. The edge computing device(s) may determine a quality metric for the agricultural inference(s). While the vehicle continues to travel across the agricultural field, and based on the quality metric: the edge computing device(s) may trigger one or more hardware adjustments to one or more of the vision sensors, or one or more adjustments in an operation of the vehicle.