Abstract
Sorghum (Sorghum bicolor L. Moench) is a C4 tropical grass that plays an essential role in providing nutrition to humans and livestock, particularly in marginal rainfall environments. The timing of head development and the number of heads per unit area are key adaptation traits to consider in agronomy and breeding but are time consuming and labor intensive to measure. We propose a two-step machine-based image processing method to detect and count the number of heads from high-resolution images captured by unmanned aerial vehicles (UAVs) in a breeding trial. To demonstrate the performance of the proposed method, 52 images were manually labeled; the precision and recall of head detection were 0.87 and 0.98, respectively, and the coefficient of determination (R2) between the manual and new methods of counting was 0.84. To verify the utility of the method in breeding programs, a geolocation-based plot segmentation method was applied to pre-processed ortho-mosaic images to extract >1000 plots from original RGB images. Forty of these plots were randomly selected and labeled manually; the precision and recall of detection were 0.82 and 0.98, respectively, and the coefficient of determination between manual and algorithm counting was 0.56, with the major source of error being related to the morphology of plants resulting in heads being displayed both within and outside the plot in which the plants were sown, i.e., being allocated to a neighboring plot. Finally, the potential applications in yield estimation from UAV-based imagery from agronomy experiments and scouting of production fields are also discussed.
Highlights
The grain yield of cereal crops is determined by accumulated processes of resource capture that support net photosynthesis across the growing season and the utilization of this source, especially in the critical period around the reproductive stage which allows establishment of a potential sink and, later in the crop, to fill those grains
Plant breeders and agronomists work collectively to modify these processes via genetics and management to develop cropping systems that optimize adaptation to different environments, those associated with drought and heat (Lobell et al, 2015; Potgieter et al, 2016)
The process of tillering provides a flexible or “plastic” response to challenging environments such as drought, and the trait of fertile head number per plant is under strong genetic control in both sorghum (Lafarge et al, 2002) and wheat (Triticum aestivum) (Mitchell et al, 2013; Dreccer et al, 2014)
Summary
The grain yield of cereal crops is determined by accumulated processes of resource capture (e.g., radiation, water, and nutrients) that support net photosynthesis across the growing season (i.e., the carbohydrate source) and the utilization of this source, especially in the critical period around the reproductive stage which allows establishment of a potential sink (grain number) and, later in the crop, to fill those grains. The process of tillering provides a flexible or “plastic” response to challenging environments such as drought, and the trait of fertile head number per plant is under strong genetic control in both sorghum (Lafarge et al, 2002) and wheat (Triticum aestivum) (Mitchell et al, 2013; Dreccer et al, 2014) All of these component traits can be measured through labor-intensive hand-sampling methods, plant breeders and agronomists doing large trials will typically only use measures of yield (via plot harvester) and individual seed mass (via sample of grains from each plot). Together with estimates of plant population, which can be done by counting the emerged plants using ground and aerial images (Gnädinger and Schmidhalter, 2017; Jin et al, 2017; Liu et al, 2017), rapid and precise estimates of fertile head number per unit area would allow researchers to estimate the fertile head number per plant as an indicator of “tillering propensity.”
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