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High Throughput Image Analytics Using Hough Transformation for Wheat Phenotyping
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Applied Engineering in Agriculture. 38(4): 627-636. (doi: 10.13031/aea.14956) @2022
Authors: James Y. Kim, Myung-Na Shin, Ji-Hyun Lee, Weon-Tai Jeon, Seungho Cho
Keywords: Calibration, Image processing, Phenotyping, Python, Software, Stitching.
RGB-based vegetation and leaf area indexes using a smartphone camera. Geo-rectification of skewed images via row detection using Hough Transformation. Open-source software to automate the image stitching and plot-level phenotypic metrics extraction.
RGB-based vegetation and leaf area indexes using a smartphone camera.
Geo-rectification of skewed images via row detection using Hough Transformation.
Open-source software to automate the image stitching and plot-level phenotypic metrics extraction.
Abstract. An agricultural field is not always accessible for plant phenotyping with existing mobile platforms due to the limited space and regulated aviation area. Smartphone-triggered ground images were collected on a wheat field that has a limited access to monitor growth conditions of four wheat varieties: Shinyoung (SY), Joseong (JS), Taewoo (TW), and Cheongwoo (CW). For field mapping during the growing season, six sets of the raw images were acquired by a smartphone in an oblique view angle and processed to transform into nadir view images. Algorithms were developed to process the raw tile images for geometric rectification via row detection using Hough Transformation. Stitching software was developed to automate the high throughput image analytics of the skewed tile images into a stitched field image through deskewing, row alignment, overlap trimming, and resizing. Plot-level metrics were extracted to analyze plant growth of the wheat varieties using a gridding method for vegetation and leaf area indexes. The processed images resulted in the successful transformation and consistency of algorithms on image alignment and stitching. Plot-level analysis indicated that SY variety performed superior to the other varieties in both vegetation and leaf area indexes and was significantly different in the canopy coverage from the least performed TW variety. The image analytic methods developed in the study offer a flexible solution to stitch and align tile images by a hand-held camera in both oblique and nadir view via user-friendly interface software for high through plant phenotyping and can be adapted to other stationary or mobile imaging platforms in greenhouse and fields.(Download PDF) (Export to EndNotes)