Abstract
Genetic improvement in sorghum breeding programs requires the assessment of adaptation traits in small-plot breeding trials across multiple environments. Many of these phenotypic assessments are made by manual measurement or visual scoring, both of which are time consuming and expensive. This limits trial size and the potential for genetic gain. In addition, these methods are typically restricted to point estimates of particular traits, such as leaf senescence or flowering and do not capture the dynamic nature of crop growth. In water-limited environments in particular, information on leaf area development over time would provide valuable insight into water use and adaptation to water scarcity during specific phenological stages of crop development. Current methods to estimate plant leaf area index (LAI) involve destructive sampling and are not practical in breeding. Unmanned aerial vehicles (UAV) and proximal-sensing technologies open new opportunities to assess these traits multiple times in large small-plot trials. We analyzed vegetation-specific crop indices obtained from a narrowband multi-spectral camera on board a UAV platform flown over a small pilot trial with 30 plots (10 genotypes randomized within 3 blocks). Due to variable emergence we were able to assess the utility of these vegetation indices to estimate canopy cover and LAI over a large range of plant densities. We found good correlations between the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) with plant number per plot, canopy cover and LAI both during the vegetative growth phase (pre-anthesis) and at maximum canopy cover shortly after anthesis. We also analyzed the utility of time-sequence data to assess the senescence pattern of sorghum genotypes known as fast (senescent) or slow senescing (stay-green) types. The Normalized Difference Red Edge (NDRE) index which estimates leaf chlorophyll content was most useful in characterizing the leaf area dynamics/senescence patterns of contrasting genotypes. These methods to monitor dynamics of green and senesced leaf area are suitable for out-scaling to enhance phenotyping of additional crop canopy characteristics and likely crop yield responses among genotypes across large fields and multiple dates.
Highlights
Sorghum is the dominant dry-land summer crop in the northeastern Australian grain belt
This paper presents results from a pilot study using a multirotor Unmanned aerial vehicles (UAV) fitted with a narrow-band multispectral camera to capture images of sorghum breeding lines with diverse canopy attributes across seven dates
We evaluated three narrow-band vegetation indices i.e., the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI) and the normalized difference red edge index (NDRE) to estimate traits, such as canopy cover, leaf area index and leaf chlorophyll content that are of particular interest to sorghum breeders in the northern grain belt of Australia
Summary
Sorghum is the dominant dry-land summer crop in the northeastern Australian grain belt. Quantitative breeding for specific traits that enhance yield in water-limited environments, will become even more important. In environments with mild to severe terminal drought stress, crops with smaller leaf area per plant have been found to have a yield advantage, as their reduced water use before flowering conserves sub-soil moisture that can be accessed during the critical grain-filling period (He et al, 2016). Breeders have positively selected for stay-green by visually rating leaf senescence after flowering This only works in trials in which the right drought conditions develop for the trait to be expressed
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