Agricultural Image Quality Filtering for Surface Condition Detection

Solution Overview

Problem

Conventional agricultural systems face challenges in obtaining reliable and accurate vision-based data due to dirty and dusty environments and low-lighting conditions, which affect the quality of images captured by imaging devices, making it difficult to estimate surface conditions in agricultural fields.

Innovation Solution

An agricultural system that utilizes imaging devices, such as stereo camera assemblies, to capture two-dimensional and three-dimensional images, and a computing system to assess image quality using pixel-related parameters and quality metrics, ensuring only high-quality images are used for estimating surface conditions like crop residue and soil clods, while discarding low-quality images.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If vision-based sensors are used to detect surface conditions in agricultural fields, then surface condition monitoring capability is improved, but image quality deteriorates due to dirty/dusty environments and low-lighting conditions

Engineering Contradiction:

Improvesurface condition monitoring capabilityVSAvoidimage quality

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The system performs preliminary assessment of image quality metrics (sharpness, exposure, noise levels) before using images for surface condition detection. Images that fail to meet quality thresholds are identified and excluded from analysis, ensuring only high-quality images contribute to surface condition estimation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where image quality metrics are continuously evaluated and used to adjust sensing operations. When quality degradation is detected (e.g., due to dust accumulation or lighting changes), the system can trigger alerts for maintenance or adjust sensor parameters to compensate for environmental conditions.

Inventive Principle:

Principle #23Feedback

2Productivity

If imaging devices operate in dirty and dusty environments, then field operation capability is improved, but image quality deteriorates due to dust on optical surfaces and in field of view

Engineering Contradiction:

Improvefield operation capabilityVSAvoidimage quality

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary assessment of image quality metrics (sharpness, exposure, noise levels) before using images for surface condition detection. Images that fail to meet quality thresholds are identified and excluded from analysis, ensuring only high-quality images contribute to surface condition estimation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system converts the harmful effect of dust and dirt into a useful indicator by using image quality metrics to identify when environmental conditions have degraded image quality. This allows the system to adaptively respond to environmental challenges rather than being disrupted by them.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

3Adaptability or versatility

If imaging devices operate in low-lighting conditions, then field operation flexibility is improved, but image quality deteriorates making computer-vision processing difficult

Engineering Contradiction:

Improvefield operation flexibilityVSAvoidimage quality

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system performs preliminary assessment of image quality metrics (sharpness, exposure, noise levels) before using images for surface condition detection. Images that fail to meet quality thresholds are identified and excluded from analysis, ensuring only high-quality images contribute to surface condition estimation.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentEP4086858B1Agricultural systems and methods using image quality metrics for vision-based detection of surface conditions

Publication Date: 2025.07.23 CNH IND BELGIUM NV

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AI summary

In one aspect, an agricultural method for monitoring surface conditions for an agricultural field includes receiving, with a computing system, an image of an imaged portion of an agricultural field, with the imaged portion of the agricultural field being represented by a plurality of pixels within the image. The method also includes identifying, with the computing system, at least one pixel-related parameter associated with the plurality of pixels within the image, determining, with the computing system, whether at least one image quality metric for the image is satisfied based at least in part on the at least one pixel-related parameter, and estimating, with the computing system, a surface condition associated with the agricultural field based at least in part on the image when it is determined that the at least one image quality metric is satisfied.