Beneficial plant-microbial interactions in agroecosystems: deciphering the rhizosphere microbial community in two field grown crops

Non-Peer Reviewe

High-Throughput UAV Image-Based Method Is More Precise Than Manual Rating of Herbicide Tolerance

The traditional visual rating system is labor-intensive, time-consuming, and prone to human error. Unmanned aerial vehicle (UAV) imagery-based vegetation indices (VI) have potential applications in high-throughput plant phenotyping. The study objective is to determine if UAV imagery provides accurate and consistent estimations of crop injury from herbicide application and its potential as an alternative to visual ratings. The study was conducted at the Kernen Crop Research Farm, University of Saskatchewan in 2016 and 2017. Fababean (Vicia faba L.) crop tolerance to nine herbicide tank mixtures was evaluated with 2 rates distributed in a randomized complete block design (RCBD) with 4 blocks. The trial was imaged using a multispectral camera with a ground sample distance (GSD) of 1.2 cm, one week after the treatment application. Visual ratings of growth reduction and physiological chlorosis were recorded simultaneously with imaging. The optimized soil-adjusted vegetation index (OSAVI) was calculated from the thresholded orthomosaics. The UAV-based vegetation index (OSAVI) produced more precise results compared to visual ratings for both years. The coefficient of variation (CV) of OSAVI was ~1% when compared to 18-43% for the visual ratings. Furthermore, Tukey’s honestly significance difference (HSD) test yielded a more precise mean separation for the UAV-based vegetation index than visual ratings. The significant correlations between OSAVI and the visual ratings from the study suggest that undesirable variability associated with visual assessments can be minimized with the UAV-based approach. UAV-based imagery methods had greater precision than the visual-based ratings for crop herbicide damage. These methods have the potential to replace visual ratings and aid in screening crops for herbicide tolerance

UAV-Based Hyperspectral Imaging Technique to Estimate Canola (Brassica napus L.) Seedpods Maturity

Identification of optimal pod maturity stage in Canola is key for maximizing seed yield, quality and also an important phenotypic trait in crop improvement programs. The conventional method is via visual inspection of seed color change. Alternatively, hyperspectral sensors have potential to determine physiological status of the crops. Therefore, the objective of the study is to estimate canola seed maturity using field-based hyperspectral imaging. For this study, five canola genotypes (NAM-0, NAM-13, NAM-17, NAM-48, and NAM-76) were selected from an experiment of 56 populations. The experimental field was imaged using a UAV-mounted hyperspectral camera (400–1,000 nm) at five growth stages starting from pod formation to near-harvest maturity (BBCH-79(S1) to 88(S5)). For each genotype; pod and seed moisture were estimated on the same day of imaging. First-order derivative was conducted on reflectance data to determine optimal spectral wavebands. As a part of this study, a new vegetation index denoted “Canola-Pod-Maturity Index (CPMI)” was developed. CPMI was evaluated in comparison with four existing vegetation indices (mNDRE, PSRI, MCARI, WBI). CPMI showed a stronger relationship ( 0.81–0.98 for pods and 0.66–0.85 for seeds) with pod and seed moisture for all the genotypes. Furthermore, the new index was able to find differences among genotypes with variable maturity times

UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies

Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective estimation of plant growth parameters. The aim of this study was to assess the potential for quantifying plant growth rate using UAV-based (unoccupied aerial vehicle) imagery collected multiple times throughout the growing season. In this study, six diverse lines of lentils were grown in three replicates of 1 m2 microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery was collected simultaneously with each manual measurement of the above-ground biomass time-point and was used to produce two-dimensional orthomosaics and three-dimensional point clouds. Non-linear logistic models were fit to multiple data collection points throughout the growing season. Overall, remotely detected vegetation area and crop volume were found to produce trends comparable to the accumulation of dry weight biomass throughout the growing season. The growth rate and G50 (days to 50% of maximum growth) parameters of the model effectively quantified lentil growth rate indicating significant potential for image-based tools to be used in plant breeding programs. Comparing image-based groundcover and vegetation volume estimates with manually measured above-ground biomass suggested strong correlations. Vegetation area measured from a UAV has utility in quantifying lentil biomass and is indicative of leaf area early in the growing season. For mid- to late-season biomass estimation, plot volume was determined to be a better estimator. Apart from traditional traits, the estimation and analysis of plant parameters not typically collected in traditional breeding programs are possible with image-based methods, and this can create new opportunities to improve breeding efficiency mainly by offering new phenotypes and affecting selection intensity