Using remote-sensing technologies to find genetic variation in photosynthetic capacity in sorghum

Abstract

Despite being a C4 crop sorghum (Sorghum bicolor (L.) Moench)) has a wide geographical distribution with adaptation to extreme climates such that its accessions are genetically diverse. Furthermore, the cereal has typically evolved in areas of limited water resources and thus alleles conferring growth under water limitation, such as alleles associated with greater photosynthetic capacity and/or efficiency may have been favourable and selected for. We used ca. 1000 exotic sorghum lines that have been introgressed with height and maturity quantitative trait loci (QTL) from a common parent (so-called sorghum conversion lines) to make the material easier to work with – and a nested association mapping population with around 1500 entries to mine this diversity for variation in alleles conferring photosynthetic capacity. In this paper, we report the use of near and remote-sensing technology, such as red (670nm), red- edge (720nm) and near infra-red (830nm) cameras mounted on unmanned aerial vehicles (UAVs) and hyperspectral sensors on a mobile phenotyping platform (GECKO) to be able to efficiently and effectively phenotype these populations for traits associated with photosynthetic capacity in replicated trials with thousands of field plots. To derive algorithms for the outputs from Lidar, sonar, thermal and hyperspectral sensors, we have collected “ground” data, such as chlorophyll content using handheld devices such as a SPAD chlorophyll and a fluorometer, measured plant height and leaf angle, as well as destructively measured leaf area index and biomass. This paper discusses the 1st season of results in developing field phenotyping methods to better characterise genetic variation for photosynthetic capacity.