Cotton Feature Identification Using Stitched Point Clouds
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Solution Overview
Problem
Existing methods for assessing crop features, such as cotton bolls, are labor-intensive, lack scalability, and are prone to subjective errors due to manual inspection.
Innovation Solution
A system and method that utilize image data from multiple cameras capturing RGB and depth information, combined with tracking data, to automatically identify features of crops by stitching point clouds, filtering data, and classifying segments to determine crop features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to assess crop features, then detailed observation and recording can be performed, but the process becomes labor-intensive and lacks scalability
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical system using cameras to capture images of crop features. The system uses computational algorithms to automatically identify, count, and measure crop features such as cotton bolls, eliminating the need for manual observation and recording while maintaining assessment accuracy
Solution Approach 2:
The system enables self-service by allowing the crop assessment process to perform its own measurement and analysis functions without human intervention. The automated image processing and feature identification algorithms independently complete the assessment task, freeing operators from manual labor while scaling productivity
2Measurement precision
If manual inspection methods are used to assess crop features, then detailed observation can be performed, but subjective errors occur due to human bias
Solution Approach 1:
The patent replaces human subjective judgment with objective computational algorithms that consistently apply the same measurement criteria to all crop features. The automated system eliminates human bias and variability, ensuring reliable and consistent assessment results across different operators and time periods
3Productivity
If multiple cameras with depth data and tracking data are used to automatically identify crop features, then productivity and objectivity are improved, but device complexity increases
Solution Approach 1:
The patent employs multiple cameras that serve multiple functions: capturing RGB images for visual identification, depth data for three-dimensional measurement, and tracking data for spatial localization. This multi-functionality allows a single system to perform comprehensive crop feature assessment without requiring separate devices for each measurement type, thereby managing complexity while maintaining high productivity
Data Source
AI summary
Systems and methods for identifying features(s) of crops are provided. One example computer-implemented method includes accessing image data specific to a cotton plant. The image data includes images of the cotton plant and, for each image, depth data indicative of a range between surfaces of the cotton plant and a camera(s) that captured the image. The image data further includes tracking data for a device including the camera(s). The method also includes stitching together point clouds, which are defined by the images of the cotton plant, and identifying brighter white segments and darker white segments in the stitched point clouds. The brighter white segments and darker white segments define segment pairs. The method then also includes appending a line between the brighter white segment and the darker white segment of each of the pairs and determining a feature of the cotton plant based on the appended lines and/or pairs.