Orthorectified Crop Image Processing for Phenotype Precision
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Solution Overview
Problem
Field-based High-Throughput Phenotyping (HTP) faces challenges in precisely measuring crop phenotypes across large agricultural fields due to the need for high measurement precision and the high cost of manual labor, which complicates distinguishing subtle differences between research plots and managing within-field variations.
Innovation Solution
A method and system for processing images of agricultural fields using a drone to capture overlapping images, which are then orthorectified and cropped to accurately determine object space positions and camera poses, allowing for precise measurement of phenotypes such as canopy cover and greenness across individual plots, reducing geometric and radiometric distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labor is used to collect phenotype data, then measurement precision can be maintained, but the cost and time required increase significantly
Solution Approach 1:
The patent replaces manual mechanical data collection with an automated aerial imaging system that captures images of agricultural plots from above. The system uses image processing algorithms to automatically extract phenotype measurements, substituting human labor with mechanical and computational systems that operate faster and more consistently.
Solution Approach 2:
The patent creates visual copies (images) of the physical plots and uses these copies to extract phenotype information through image analysis. Instead of manually measuring physical plots, the system captures aerial images and processes them computationally to obtain precise measurements of crop characteristics.
2Productivity
If aerial images are captured without correction, then data collection is faster, but geometric and radiometric distortion reduces measurement accuracy
Solution Approach 1:
The patent applies orthorectification and radiometric correction to the aerial images before extracting phenotype measurements. These preliminary processing steps correct geometric distortions caused by aerial perspective and radiometric variations, ensuring that subsequent measurements are accurate while maintaining the efficiency of remote sensing.
Data Source
AI summary
A method for processing images of an agricultural field is disclosed that enables accurate phenotype measurements for crops planted in each research plot of the agricultural field. The method comprises receiving a plurality of input images of the agricultural field, calculating and refining object space coordinates for matched key points in the input images and an object space camera pose for each input image, calculating and refining object space center points for the research plots based on a user-defined plot layout, and generating output images of individual research plots that are centered, cropped, orthorectified, and oriented in alignment with planted rows of crops. Based on the output images, accurate phenotype measurements for crops planted in each research plot can be determined. The method advantageously minimizes row-offset errors, variations in canopy cover and color between images, geometric and radiometric distortion, and computational memory requirements, while facilitating parallelized image processing and analysis.