Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Remote sensing of biological soil crust under simulated climate change manipulations in the Mojave Desert

Earth\u27s arid and semiarid ecosystems are subject to novel combinations of disruptive factors and unprecedented rates of change. Biotic soil crust is believed to be sensitive to impacts caused by land use and climate changes. This study examined the potential for spectral detection of different biological soil crusts (BSC: cyanobacteria, moss and lichen) and bare soil components at a long-term manipulative experiment at the Mojave Global Change Facility (MGCF) in southwestern Nevada. We evaluated the potential for spectral detection of experimental treatments using laboratory and field measured reflectance spectra in the second and third year of the experiment, and airborne hyperspectral data obtained in the third year of the manipulations for soil disturbance, increased summer rainfall, and dry nitrogen deposition. Laboratory spectra of individual components of biological soil crust and bare soil measured under controlled laboratory conditions were spectrally different over much of the spectrum and exhibited features at 0.42, 0.50, and 0.68 μm, which could differentiate these materials. Field measured spectra were more similar in overall shape in each of the MGCF treatments and individual BSC could not be distinguished. The field spectra most closely resemble cyanobacteria from laboratory measurements, which are known to cover up to 60% of the inter-shrub spaces. There were significant treatment differences between control, soil disturbance, and irrigation treatments in field spectral measurements and a spectral feature in the 2.00–2.08 μm region could distinguish these treatments. The treatments were also apparent in high spatial resolution (~ 4 m ground IFOV) airborne hyperspectral imagery using a minimum noise fraction (MNF) analysis, although treatments were not distinct in terms of laboratory or field-based specific features. Disturbance treatments were easily apparent in color-infrared imagery although other treatments were not distinguished. Three-band composites from a MNF analysis and classification images of the six most significant MNF bands for treatment differences, revealed disturbed and irrigation treatments and combinations of these with nitrogen treatments can be observed but control treatments were not separated from the untreated background. Nitrogen treatments were generally not significantly different from controls unless combined with irrigation or disturbance treatments. These data suggest that hyperspectral imagery could be used to monitor local and perhaps regional changes in biological soil crust in the southwestern deserts of the United States, even if crust components are not individually detected

Wheat ear counting in-field conditions High throughput and low-cost approach using RGB images

[Background], The number of ears per unit ground area (ear density) is one of the main agronomic yield components in determining grain yield in wheat. A fast evaluation of this attribute may contribute to monitoring the efficiency of crop management practices, to an early prediction of grain yield or as a phenotyping trait in breeding programs. Currently the number of ears is counted manually, which is time consuming. Moreover, there is no single standardized protocol for counting the ears. An automatic ear-counting algorithm is proposed to estimate ear density under field conditions based on zenithal color digital images taken from above the crop in natural light conditions. Field trials were carried out at two sites in Spain during the 2014/2015 crop season on a set of 24 varieties of durum wheat with two growing conditions per site. The algorithm for counting uses three steps: (1) a Laplacian frequency filter chosen to remove low and high frequency elements appearing in an image, (2) a Median filter to reduce high noise still present around the ears and (3) segmentation using Find Maxima to segment local peaks and determine the ear count within the image. [Results], The results demonstrate high success rate (higher than 90%) between the algorithm counts and the manual (image-based) ear counts, and precision, with a low standard deviation (around 5%). The relationships between algorithm ear counts and grain yield was also significant and greater than the correlation with manual (field-based) ear counts. In this approach, results demonstrate that automatic ear counting performed on data captured around anthesis correlated better with grain yield than with images captured at later stages when the low performance of ear counting at late grain filling stages was associated with the loss of contrast between canopy and ears. [Conclusions], Developing robust, low-cost and efficient field methods to assess wheat ear density, as a major agronomic component of yield, is highly relevant for phenotyping efforts towards increases in grain yield. Although the phenological stage of measurements is important, the robust image analysis algorithm presented here appears to be amenable from aerial or other automated platforms

Leaf dorsoventrality as a paramount factor determining spectral performance in field-grown wheat under contrasting water regimes

The effects of leaf dorsoventrality and its interaction with environmentally induced changes in the leaf spectral response are still poorly understood, particularly for isobilateral leaves. We investigated the spectral performance of 24 genotypes of field-grown durum wheat at two locations under both rainfed and irrigated conditions. Flag leaf reflectance spectra in the VIS-NIR-SWIR (visible–near-infrared–short-wave infrared) regions were recorded in the adaxial and abaxial leaf sides and at the canopy level, while traits providing information on water status and grain yield were evaluated. Moreover, leaf anatomical parameters were measured in a subset of five genotypes. The spectral traits studied were more affected by the leaf side than by the water regime. Leaf dorsoventral differences suggested higher accessory pigment content in the abaxial leaf side, while water regime differences were related to increased chlorophyll, nitrogen, and water contents in the leaves in the irrigated treatment. These variations were associated with anatomical changes. Additionally, leaf dorsoventral differences were less in the rainfed treatment, suggesting the existence of leaf-side-specific responses at the anatomical and biochemical level. Finally, the accuracy in yield prediction was enhanced when abaxial leaf spectra were employed. We concluded that the importance of dorsoventrality in spectral traits is paramount, even in isobilateral leaves

Post-green revolution genetic advance in durum wheat The case of Spain

This paper addresses the question of whether there has been any genetic gain in yield for durum wheat released in Spain after the Green Revolution and assesses the agronomical and physiological traits associated with evolution of the crop during this time. Field experiments were carried out with a wide range of durum wheat cultivars (released in Spain from 1980 to 2009) and were conducted in different sites embracing a wide range of growing temperatures and water regimes at Aranjuez and Zamadueñas during three consecutive growing seasons (2013/14, 2014/15, 2015/16) under rainfed and supplemental irrigation and at Coria for two consecutive seasons (2014/15 and 2015/16) under rainfed conditions alone. Grain yield increased with the year of release of cultivars at a rate of 24 kg ha−1 y−1 (0.44% y−1) from 1980 to 2003, with no clear additional improvements thereafter. The moderate grain yield improvement from 1980 and 2003 was associated with kernels m−2 and kernels spike−1, with an increase of 117 kernels m−2 y−1 and 0.24 kernels spike−1 y−1, respectively. Moreover, aerial biomass at harvest and grain nitrogen yield increased with the year of release of cultivars for the entire period. However, no differences were found for thousand kernel weight, number of spikes m−2, days to heading, plant height, harvest index, canopy temperature depression, carbon isotope discrimination or grain nitrogen concentration. Overall, these results indicated that the rate of genetic progress in the yield of durum wheat in Spain after the Green Revolution has been low and has even stopped during the last decade, while no clear trend in some grain quality traits (TKW and grain N concentration) was recorded. However, the absolute and relative genetic gains estimated for yield were positively associated with the average mean and maximum daily temperatures from sowing to harvest of the testing site, which suggest that breeding has been performed under high-temperature environments