UAV-Based Thermal Imaging for High-Throughput Field Phenotyping of Black Poplar Response to Drought

Abstract

Poplars are fast-growing, high-yielding forest tree species, whose cultivation as second-generation biofuel crops is of increasing interest and can efficiently meet emission reduction goals. Yet, breeding elite poplar trees for drought resistance remains a major challenge. Worldwide breeding programs are largely focused on intra/interspecific hybridization, whereby Populus nigra L. is a fundamental parental pool. While high-throughput genotyping has resulted in unprecedented capabilities to rapidly decode complex genetic architecture of plant stress resistance, linking genomics to phenomics is hindered by technically challenging phenotyping. Relying on unmanned aerial vehicle (UAV)-based remote sensing and imaging techniques, high-throughput field phenotyping (HTFP) aims at enabling highly precise and efficient, non-destructive screening of genotype performance in large populations. To efficiently support forest-tree breeding programs, ground-truthing observations should be complemented with standardized HTFP. In this study, we develop a high-resolution (leaf level) HTFP approach to investigate the response to drought of a full-sib F2 partially inbred population (termed here ‘POP6’), whose F1 was obtained from an intraspecific P. nigra controlled cross between genotypes with highly divergent phenotypes. We assessed the effects of two water treatments (well-watered and moderate drought) on a population of 4603 trees (503 genotypes) hosted in two adjacent experimental plots (1.67 ha) by conducting low-elevation (25 m) flights with an aerial drone and capturing 7836 thermal infrared (TIR) images. TIR images were undistorted, georeferenced, and orthorectified to obtain radiometric mosaics. Canopy temperature (Tc) was extracted using two independent semi-automated segmentation techniques, eCognition- and Matlab-based, to avoid the mixed-pixel problem. Overall, results showed that the UAV platform-based thermal imaging enables to effectively assess genotype variability under drought stress conditions. Tc derived from aerial thermal imagery presented a good correlation with ground-truth stomatal conductance (gs) in both segmentation techniques. Interestingly, the HTFP approach was instrumental to detect drought-tolerant response in 25% of the population. This study shows the potential of UAV-based thermal imaging for field phenomics of poplar and other tree species. This is anticipated to have tremendous implications for accelerating forest tree genetic improvement against abiotic stress.

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

“…The ability of these non-visible bands to model such subtle changes in plant condition should contribute to better discrimination of panicles from objects that are spectrally similar in the visible spectrum such as ground and dried foliage. Thermal imagery also presents another interest option, especially for characterizing canopy temperature [66][67][68]. Given the high saliency of sorghum panicles compared to other features in a field, significant temperature gradients may be expected which should be amenable to object discrimination image analysis approaches as presented in this study.…”

On the Use of Unmanned Aerial Systems for Environmental Monitoring

“…Increasing miniaturization allows multispectral, hyperspectral, and thermal imaging, as well as Synthetic Aperture Radar (SAR) and LiDAR (Light Detection and Ranging) sensing to be conducted from UAS. As examples of recent UAS-based environmental monitoring applications, work has focused on (a) land cover mapping [18,19]; (b) vegetation state, phenology, and health [20,21]; (c) precision farming/agriculture [22][23][24]; (d) monitoring crop growth, and invasive species infestation [25,26]; (e) atmospheric observations [27]; (f) disaster mapping [28]; (g) soil erosion [29,30]; (h) mapping soil surface characteristics [31,32]; and (i) change detection [33].…”

“…For example, Quilter et al [129] used UAS for monitoring streams and riparian restoration projects in inaccessible areas on Chalk Creek (Utah). Knoth et al [130] applied a UAS-based NIR remote sensing approach to monitor a restored cut-over bog and Ludovisi et al [21] also used TIR data to determine the response of forest to drought in relation to forest tree breeding programs and genetic improvement.…”

Breeding progress and preparedness for mass‐scale deployment of perennial lignocellulosic biomass crops switchgrass, miscanthus, willow and poplar

“…High-throughput plant phenotyping facilities provide accurate screening of thousands of plant breeding lines, clones or populations over time (Fu, 2015) are critical for accelerating genomics-based breeding. Automated image collection and analysis, phenomics technologies allow accurate and nondestructive measurements of a diversity of phenotypic traits in large breeding populations (Gegas, Gay, Camargo & Doonan, 2014;Goggin, Lorence, & Topp, 2015;Ludovisi et al, 2017;Shakoor, Lee, & Mockler, 2017). One important consideration is the identification of relevant and quantifiable target traits that are early diagnostic indicators of biomass yield.…”

“…Good progress has been made in elucidating these underpinning morpho-physiological traits that are amenable to remote sensing in Populus (Harfouche, Meilan, & Altman, 2014;Rae, Robinson, Street, & Taylor, 2004). More recently, Ludovisi et al (2017) developed a novel methodology for field phenomics of drought stress in a P. nigra F 2 partially inbred population using thermal infrared images recorded from an unmanned aerial vehicle-based platform.…”