A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery

Abstract

The dynamics of the Green Leaf Area Index (GLAI) is of great interest for numerous applications such as yield prediction and plant breeding. We present a high-throughput model-assisted method for characterizing GLAI dynamics in maize ( Zea mays subsp. mays ) using multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV). Two trials were conducted with a high diversity panel of 400 lines under well-watered and water-deficient treatments in 2016 and 2017. For each UAV flight, we first derived GLAI estimates from empirical relationships between the multispectral reflectance and ground level measurements of GLAI achieved over a small sample of microplots. We then fitted a simple but physiologically sound GLAI dynamics model over the GLAI values estimated previously. Results show that GLAI dynamics was estimated accurately throughout the cycle (R 2 > 0.9). Two parameters of the model, biggest leaf area and leaf longevity, were also estimated successfully. We showed that GLAI dynamics and the parameters of the fitted model are highly heritable (0.65 ≤ H 2 ≤ 0.98), responsive to environmental conditions, and linked to yield and drought tolerance. This method, combining growth modeling, UAV imagery and simple non-destructive field measurements, provides new high-throughput tools for understanding the adaptation of GLAI dynamics and its interaction with the environment. GLAI dynamics is also a promising trait for crop breeding, and paves the way for future genetic studies.

National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm

“…From the 6-8 leaves stage, the stem starts to elongate, and the number of leaves continue to increase until the end of the vegetative stage, leading to a rapid increase in canopy size [79,84]. By the time of the tasseling stage (i.e., tassels are completely visible), plants have reached their maximum height and canopy size [85,93]. Silks then appear, marking the beginning of the reproductive stage.…”

Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops

“…In soybean, an unmanned aerial vehicle (UAV, aka drone) with an RGB camera measured canopy coverage in the field across several days after planting for genomewide association analysis and genomic selection (Xavier et al, 2017;Hearst, 2019;Moreira et al, 2019). Blancon et al (2019) characterized the dynamics of the green-leaf-area index in a diversity panel of maize under well-watered and water-deficient treatments using multispectral imagery acquired from a UAV throughout the growth cycle. Thermal and hyperspectral sensors mounted to a manned aircraft were used to extract canopy temperature and vegetation indexes of more than 500 lines of wheat in five field environments over a range of dates (Rutkoski et al, 2016).…”

Breeding for Abiotic Stress Adaptation in Chickpea (Cicer arietinum L.): A Comprehensive Review

“…phenology, early vigor, crop growth status, water content, biomass, and yield potential [117,118]. A plethora of optical devices such as passive (FieldSpec spectroradiometer; [119]) and active sensors (Crop Circle; [120]); red, green and blue (RGB) [121,122], multispectral [123], hyperspectral camera [124] and thermal camera [125] are available. The Light Detection and Ranging (LiDAR; [126]) and LeasyScan PlantEye ® [127] scanning systems emit laser pulses that capture the timing and intensity of the pulse bouncing back from the crop canopy to reconstruct 3D…”

“…Conventionally, the stay-green trait is assessed by visual scoring, which is subjective, labor intensive and prone to human errors and bias. Proximal and remote sensing technology using sensors and cameras can be a method of choice for HTP screening of stay-green phenotypes of different crop species [123,143] and chickpea [145].…”