Abstract
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.
Highlights
In this age of increasing concerns about population growth, energy demands and environmental risks, the need to expand energy resources in a sustainable manner is intensifying
Carbon dioxide emissions from fossil fuels have led to concerns about rising levels of greenhouse gases [3], increasing the importance of developing alternative energy sources, like dedicated energy crops that reduce net carbon dioxide emissions
The stalk thickness values determined through image analysis were compared to the manually-measured values and the percent error calculated for each plant and each date (Table 1)
Summary
In this age of increasing concerns about population growth, energy demands and environmental risks, the need to expand energy resources in a sustainable manner is intensifying. Carbon dioxide emissions from fossil fuels have led to concerns about rising levels of greenhouse gases [3], increasing the importance of developing alternative energy sources, like dedicated energy crops that reduce net carbon dioxide emissions. One solution to these issues is to increase energy crop production by maximizing crop output per unit area. The advent of sensing and robotics has provided the potential to drastically accelerate the advances of breeding and genetics through “high-throughput phenotyping” (HTP).
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