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J. Imaging 2016, 2(1), 4;

Imaging for High-Throughput Phenotyping in Energy Sorghum

Department of Biological & Agricultural Engineering, Texas A&M University, 2117 TAMU, College Station, TX 77843-2117, USA
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Francisco Rovira-Más
Received: 3 November 2015 / Revised: 15 January 2016 / Accepted: 15 January 2016 / Published: 26 January 2016
(This article belongs to the Special Issue Image Processing in Agriculture and Forestry)
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The increasing energy demand in recent years has resulted in a continuous growing interest in renewable energy sources, such as efficient and high-yielding energy crops. Energy sorghum is a crop that has shown great potential in this area, but needs further improvement. Plant phenotyping—measuring physiological characteristics of plants—is a laborious and time-consuming task, but it is essential for crop breeders as they attempt to improve a crop. The development of high-throughput phenotyping (HTP)—the use of autonomous sensing systems to rapidly measure plant characteristics—offers great potential for vastly expanding the number of types of a given crop plant surveyed. HTP can thus enable much more rapid progress in crop improvement through the inclusion of more genetic variability. For energy sorghum, stalk thickness is a critically important phenotype, as the stalk contains most of the biomass. Imaging is an excellent candidate for certain phenotypic measurements, as it can simulate visual observations. The aim of this study was to evaluate image analysis techniques involving K-means clustering and minimum-distance classification for use on red-green-blue (RGB) images of sorghum plants as a means to measure stalk thickness. Additionally, a depth camera integrated with the RGB camera was tested for the accuracy of distance measurements between camera and plant. Eight plants were imaged on six dates through the growing season, and image segmentation, classification and stalk thickness measurement were performed. While accuracy levels with both image analysis techniques needed improvement, both showed promise as tools for HTP in sorghum. The average error for K-means with supervised stalk measurement was 10.7% after removal of known outliers. View Full-Text
Keywords: image analysis; stalk thickness; K-means; minimum distance image analysis; stalk thickness; K-means; minimum distance

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Batz, J.; Méndez-Dorado, M.A.; Thomasson, J.A. Imaging for High-Throughput Phenotyping in Energy Sorghum. J. Imaging 2016, 2, 4.

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