Drought is one of the biggest concerns in agriculture due to the projected reduction of global freshwater supply with a concurrent increase in global food demand. Roots can significantly contribute to improving drought adaptation and productivity. Plants increase water uptake by adjusting root architecture and cooperating with symbiotic soil microbes. Thus, emphasis has been given to root architectural responses and root–microbe relationships in drought-resilient crop development. However, root responses to drought adaptation are continuous and complex processes and involve additional root traits and interactions among themselves. This review comprehensively compiles and discusses several of these root traits such as structural, physiological, molecular, hydraulic, anatomical, and plasticity, which are important to consider together, with architectural changes, when developing drought resilient crop varieties. In addition, it describes the significance of root contribution in improving soil structure and water holding capacity and its implication on long-term resilience to drought. In addition, various drought adaptive root ideotypes of monocot and dicot crops are compared and proposed for given agroclimatic conditions. Overall, this review provides a broader perspective of understanding root structural, physiological, and molecular regulators, and describes the considerations for simultaneously integrating multiple traits for drought tolerance and crop improvement, under specific growing environments.

Unmanned aerial vehicle (UAV) based hyperspectral imaging is a relatively new remote sensing technology. Near-earth spectroscopy from UAV-hyperspectral systems addresses the limitations of resolution, signal-to-noise ratio, and cloud cover from traditional satellite or airborne platforms. The technology is increasingly being used to obtain fast and accurate information. Recently developed methods for data acquisition and pre-processing have mostly focused on agriculture and forestry applications. This paper addresses the challenges in the methods of sensor calibration, data acquisition, radiometric correction for illumination variation, mosaicking and geometric correction for UAV-hyperspectral imaging of highly heterogeneous environments, such as swamps. A pre-processing workflow was developed to address the specific issues of monitoring the heterogenous distribution of the swamp vegetation and assess its condition. The workflow was used to produce reflectance products and vegetation indices. The developed framework would be useful in remote environments where satellite or airborne hyperspectral observations are limited due to the presence of spectrally complex environment.

The development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.

Efficient, precise and timely measurement of plant traits is important in the assessment of a breeding population. Estimating crop biomass in breeding trials using high-throughput technologies is difficult, as reproductive and senescence stages do not relate to reflectance spectra, and multiple growth stages occur concurrently in diverse genotypes. Additionally, vegetation indices (VIs) saturate at high canopy coverage, and vertical growth profiles are difficult to capture using VIs. A novel approach was implemented involving a fusion of complementary spectral and structural information, to calculate intermediate metrics such as crop height model (CHM), crop coverage (CC) and crop volume (CV), which were finally used to calculate dry (DW) and fresh (FW) weight of above-ground biomass in wheat. The intermediate metrics, CHM (R² = 0.81, SEE = 4.19 cm) and CC (OA = 99.2%, Κ = 0.98) were found to be accurate against equivalent ground truth measurements. The metrics CV and CV×VIs were used to develop an effective and accurate linear regression model relationship with DW (R² = 0.96 and SEE = 69.2 g/m²) and FW (R² = 0.89 and SEE = 333.54 g/m²). The implemented approach outperformed commonly used VIs for estimation of biomass at all growth stages in wheat. The achieved results strongly support the applicability of the proposed approach for high-throughput phenotyping of germplasm in wheat and other crop species.