Plot Gap Identification Using Plant Presence Heat Maps
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Solution Overview
Problem
Inaccurate mapping of crop growth data in fields due to unreliable position data and plot length variations, leading to incorrect identification of gaps between seed plots, which can misrepresent growth performance and genotype comparisons.
Innovation Solution
A field data collection and analysis system that uses plant presence values aggregated from sensor data to identify real gaps between plots, differentiating them from false gaps through heat maps and field characteristic data, ensuring accurate mapping and comparison of crop growth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If field data is collected using positioning systems and plot length data, then mapping information to field locations is enabled, but measurement precision deteriorates due to unreliable position data and plot length variations
Solution Approach 1:
The patent replaces mechanical positioning systems (GPS, total stations) with a vision-based optical system. The field data collection system uses cameras to capture images of plants and crops, and automatically determines plot boundaries and locations through image processing and pattern recognition, eliminating reliance on unreliable mechanical positioning data.
Solution Approach 2:
The system uses the field data itself (plant images and growth patterns) to identify and map plot boundaries. The plants and crops serve as natural markers that the system detects and uses for self-localization and plot delineation, making the system self-sufficient and independent of external positioning infrastructure.
2Measurement precision
If gaps between plots are identified using position data and plot length data, then plot segmentation is enabled, but measurement precision deteriorates due to incorrect gap identification from false gaps
Solution Approach 1:
The patent replaces computational analysis of positioning data with visual analysis of field images. The system identifies gaps between plots by detecting patterns in captured images, such as changes in plant density, color, or structural features, rather than relying on complex data processing of GPS coordinates and plot length measurements.
Solution Approach 2:
The system utilizes color and visual characteristics of plants and soil to identify plot boundaries and gaps. Different plots may have different plant varieties with distinct color patterns, and gaps are identified as regions with characteristic visual properties (such as bare soil color or reduced plant density) that differ from planted areas.
3Reliability
If field data collection system navigates through the field collecting data, then comprehensive field coverage is achieved, but reliability deteriorates due to collection failures and incorrect data mapping
Solution Approach 1:
The field data collection system performs self-localization and self-correction by using visual features in the field environment. When navigation or data collection anomalies occur, the system automatically detects them through image analysis and corrects its position and data mapping without external intervention, maintaining reliability while continuing operation.
Solution Approach 2:
The system continuously monitors the quality and consistency of collected field data through real-time image processing. When collection failures or mapping errors are detected (such as unexpected gaps or pattern inconsistencies), the system generates feedback signals to trigger re-collection or correction, ensuring data reliability without significantly impacting overall productivity.
Data Source
AI summary
Field data is collected of a field. Each instance of field data contains information that can be used to determine a value corresponding to whether or not a plant is present or absent in a particular location and is referred to as a plant presence value. The plant presence values are aggregated using the position data associated with each instance of field data to generate aggregated plant presence values. Gaps between plots are identified based partly on variations in the plant presence values within the aggregated field data. Information known about a field can be used to heuristically identify gaps in a seed line or used to eliminate locations on a seed line that may look like a gap based on low plant presence values. The aggregated plant presence values can be presented as a heat map of plant presence values showing the relative plant density of the field.