Imaging for High-Throughput Phenotyping in Energy Sorghum

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.

Seedling growth and fall armyworm feeding preference influenced by dhurrin production in sorghum

“…Many studies have demonstrated the value of image-based sorghum phenotyping technologies for assessment of plant growth and development (Neilson et al 2015 ; Batz et al 2016 ; Thapa et al 2018 ; Masjedi et al 2020 ). In this study, the ARIS phenotyping platform was shown to be useful for collecting nondestructive measurements of green plant area (GPA) in sorghum.…”

Sorghum Germplasm Resources Characterization and Trait Mapping

“…Recently, Batz et al (2016) demonstrated imaging for high-throughput phenotyping in energy sorghum. At ICRISAT, a high-throughput phenotyping platform called the "LeasyScan" facility has been established to measure leaf area quicker so as to access the dynamics of leaf development and leaf conductance, traits that are the focus for plant drought adaptation in ICRI-SAT mandate crops including sorghum.…”

Autonomous Mobile Ground Control Point Improves Accuracy of Agricultural Remote Sensing through Collaboration with UAV

“…Since UAV platforms can significantly improve the spatial and temporal resolution of field images, they are increasingly used in precision agriculture to maximize crop yield and to protect crops from losses [19,20]. To accelerate breeding and genetics research, many types of imaging sensors, such as RGB [21,22], multispectral [23,24], and thermal [25,26], are used on UAVs for mapping phenotypes at the plot or plant scale resolution [27][28][29]. The increasing capabilities of such remote sensing techniques are continuously pushing boundaries improves the accuracy of plant-height estimates [54].…”