Combine Stalk Sensors for Missing and Late-Emerged Plant Detection
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Solution Overview
Problem
Existing yield maps in agriculture do not accurately detect missing or late-emerged plants, leading to incorrect assessment of yield issues and potential economic losses, as they treat missing plants as lower yield areas rather than distinct problems requiring different corrective actions.
Innovation Solution
Implementing stalk sensors on combine harvesters to count and measure corn stalks on a row-by-row basis, using wheels with pulse sensors to determine stalk size and engage with brakes and proximity sensors to accurately differentiate between healthy and late-emerged stalks, coupled with a data visualization system to display yield data including missing and late-emerged plants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional yield maps are used to assess crop yield, then yield data can be obtained, but missing or late-emerged plants cannot be accurately detected and are incorrectly classified as lower yield areas
Solution Approach 1:
The yield assessment is segmented into distinct plant categories: healthy plants, missing plants, and late-emerged plants. The sensor system counts individual stalks and measures their dimensions separately, allowing each category to be identified and analyzed independently rather than aggregated into a single yield metric.
Solution Approach 2:
The patent replaces traditional mechanical yield monitoring with optical and electronic sensing systems. Sensors capture stalk presence, count, and dimensional data, which are then processed by computer systems to generate differentiated yield maps that distinguish between missing plants and low-yielding healthy plants.
2Measurement precision
If stalk sensors and data visualization systems are implemented, then accurate detection of missing and late-emerged plants is achieved, but device complexity increases
Solution Approach 1:
The sensor system performs multiple functions: counting stalks, measuring stalk dimensions, identifying missing plants, detecting late-emerged plants, and generating differentiated yield maps. This multi-functionality consolidates what would otherwise require separate systems into a single integrated platform.
Solution Approach 2:
The system automatically processes raw sensor data through algorithms that identify plant categories, calculate yield metrics, and generate visualizations without manual intervention. The computer system self-manages data collection, processing, and presentation, reducing the need for operator expertise in data interpretation.
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
Provides accurate yield mapping by distinguishing between healthy and late-emerged plants, allowing for precise identification of yield loss causes and enabling targeted corrective actions to improve future yields.
Implementation Method 1
at least one pulse sensor in communication with the wheel and constructed and arranged to detect degrees of rotation of the at least one wheel
Data Source
AI summary
The disclosure relates to agricultural systems. The systems have stalk sensor assemblies and/or data visualization systems. The stalk sensor assemblies are configured for assessing the size and other characteristics about crops, such as corn and other grains entering an agricultural implement, such as a harvester. The stalk sensor assemblies may use an estimation of the stalk perimeter to establish stalk size and therefore further features about the crop. The visualization system utilizes data from the stalk sensor assemblies to calculate and display relevant information about the crop.