3D Crop Biometrics Modeling for Non-Invasive Field Measurement
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Solution Overview
Problem
Current methods for detecting crop biometrics are invasive or rely on sparse, manually intensive, and inaccurate non-invasive techniques, which fail to provide detailed and frequent assessments of crop health and growth status.
Innovation Solution
A system utilizing an unmanned vehicle with an imaging device and a crop modeling device that generates a three-dimensional model of crops from images, defining locations and orientations of leaves and stems, and determining biometric parameters such as plant height and leaf count.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive techniques are used to estimate plant biometrics, then measurement precision is improved, but the plants are destroyed
Solution Approach 1:
The patent creates a three-dimensional digital model (copy) of the plant based on multiple images, allowing biometric measurements to be taken from the model rather than the physical plant. This copying approach enables accurate measurement of plant height, leaf area, and other biometrics without physically damaging or destroying the actual plant.
Solution Approach 2:
The patent replaces physical/mechanical measurement methods (such as direct measurement tools that require plant destruction) with an optical-based imaging and computational modeling system. Multiple images are captured and processed to generate a 3D model, substituting mechanical extraction methods with non-contact optical measurement.
2Object-affected harmful factors
If non-invasive techniques are used to estimate plant biometrics, then plant integrity is preserved, but measurement precision and detail are reduced
Solution Approach 1:
The patent transitions from two-dimensional image data to a three-dimensional model representation of the plant. By constructing a 3D model from multiple 2D images, the system preserves plant integrity while gaining accurate spatial information about plant structure, leaf orientation, and biometric parameters that cannot be obtained from single-plane measurements.
Solution Approach 2:
The patent segments the plant model into distinct components such as leaves, stems, and other structural elements. This segmentation allows for precise measurement of individual biometric parameters (leaf area, plant height, stem thickness) while maintaining the overall integrity of the non-invasive approach.
3Device complexity
If manual physical measurements are used, then equipment complexity is reduced, but productivity and measurement frequency are limited
Solution Approach 1:
The system captures multiple images automatically and processes them through automated algorithms to generate the 3D model and extract biometric parameters without requiring manual intervention for each measurement. The computational model performs the measurement tasks automatically, enabling high-frequency measurements without proportional increases in manual labor or system complexity.
4Quantity of substance
If sparse random physical measurements are used, then measurement cost is reduced, but information completeness and granularity are insufficient
Solution Approach 1:
The three-dimensional model serves multiple functions simultaneously: it provides overall plant size measurements, individual leaf area calculations, stem dimension analysis, and spatial orientation data. A single 3D model generation process extracts comprehensive biometric information across multiple parameters, eliminating the need for separate measurement campaigns for different crop attributes.
Data Source
AI summary
Systems, techniques, and devices for detecting plant biometrics, for example, plants in a crop field. An imaging device of an unmanned vehicle may be used to generate a plurality of images of the plants, and the plurality of images may be used to generate a 3D model of the plants. The 3D model may define locations and orientations of leaves and stems of plants. The 3D model may be used to determine at least one biometric parameter of at least one plant in the crop. Such detection of plant biometrics may facilitate the automation of crop monitoring and treatment.