Design, construction and field testing of a manually feeding semiautomatic sugarcane dud chipper.

Sugarcane is the main sugar crop, and sugar is an important agricultural product in Egypt. There are many problems with the technology used in the current planting method of sugarcane, which has a great impact on the planting quality of sugarcane, which have a series of problems, such as low cutting efficiency and poor quality. Therefore, the aim of the current study was to design, construct, and field testing of a semiautomatic sugarcane bud chipper assisted with pivot knives for cutting sugarcane buds and germinating them in plastic trays inside a greenhouse until they reached an average length of 35 cm, and then planting them in the field. In the field tests five cutting speeds (35, 40, 45, 50, and 56 rpm. (Revolution Per minute), three cutting knives (1.5, 2.0, and 2.5 mm) were used for cutting sugarcane stalks with four different diameters (1.32, 1.82, 2.43, and 2.68 cm). The obtained results showed that the values of the damage index and invisible losses were within acceptable limits (ranging between - 1.0 and 0.0) for all the variables under the test. Still, the lowest damage index and invisible losses were recorded with the buds that were cut with a knife of 1.5 mm thickness and cutting speeds less than 50 rpm. The skipping rate increases with the increase in cutting speed and stalk diameter, ranging between 0.0 to 13%. The maximum machine productivity was 110 Buds per minute at a cutting speed of 35 rpm and stalk diameter of 1.32 cm. The paper's findings have important application values for promoting the designing and development of sugarcane bud chipper and sugarcane planting technology in the future.

Impacts of algae supplements (Arthrospira & Chlorella) on growth, nutrient variables, intestinal efficacy, and antioxidants in New Zealand white rabbits.

An 8-week trial to examine the impacts of Arthrospira platensis and Chlorella vulgaris on the growth, nutrient aspects, intestinal efficacy, and antioxidants of 75 New Zealand white male rabbits (initial body weight = 665.93 ± 15.18 g). Herein the study was designed in one-way ANOVA to compare the effects of the two algae species with two levels of supplementations in the feeds of New Zealand white rabbits. The rabbits were divided into five groups (n = 15/group), where the first group was allocated as the control group (Ctrl) while the second and third groups received A. platensis at 300 or 500 mg/kg diet (Ap300 or Ap500). The fourth and fifth groups fed C. vulgaris at 300 or 500 mg/kg diet (Ch300 or Ch500). The basal diet rabbits exhibited the lowest values of weight, lipase, protease, and the highest feed conversion ratio, which improved noticeably with algae addition, particularly with Ap500, Ch300, and Ch500. All tested groups showed normal intestinal structure. Amylase potency, hematological indicators, and serum biochemistry revealed non-significant variation except for a higher serum total protein and lower total cholesterol in algal groups. The best GPx existed in groups fed algal diets, while favorable SOD and CAT efficiency occurred at the higher level of Arthrospira and both levels of Chlorella. In conclusion, incorporating Arthrospira or Chlorella in the diet of New Zealand white rabbits improved performance, nutrient utilization, intestinal efficacy, and antioxidants. Arthrospira (Ap500) and Chlorella (Ch300 or Ch500) have almost the same beneficial effect on rabbit performance.

Response of varied rice genotypes on cell membrane stability, defense system, physio-morphological traits and yield under transplanting and aerobic cultivation.

Aerobic rice cultivation progresses water productivity, and it can save almost 50% of irrigation water compared to lowland rice with the appropriate development of genotypes and management practices. Two field trials were conducted during 2020, and 2021 seasons to determine the validation of different rice varieties under aerobic cultivation based on their plant defense system, physio-morphological traits, stress indices, grain yield, and water productivity. The experiments were designed in a split-plot design with four replications. Two planting methods, transplanting and aerobic cultivation, were denoted as the main plots, and ten rice genotypes were distributed in the subplots. The results revealed that the planting method varied significantly in all measured parameters. The transplanting method with well watering had the highest value of all measured parameters except leaf rolling, membrane stability index, antioxidant, proline, and the number of unfilled grains. EHR1, Giza179 and GZ9399 as well as A22 genotypes a chief more antioxidant defense system that operated under aerobic conditions. Giza179, EHR1, GZ9399, and Giza178 showed high cell membrane stability and subsequently high validation under such conditions, and also showed efficiency in decreasing water consumption and improving water use efficiency. In conclusion, this study proves that Giza179, EHR1, GZ9399, Giza178, and A22 are valid genotypes for aerobic conditions.

Combining Hyperspectral Reflectance Indices and Multivariate Analysis to Estimate Different Units of Chlorophyll Content of Spring Wheat under Salinity Conditions.

Although plant chlorophyll (Chl) is one of the important elements in monitoring plant stress and reflects the photosynthetic capacity of plants, their measurement in the lab is generally time- and cost-inefficient and based on a small part of the leaf. This study examines the ability of canopy spectral reflectance data for the accurate estimation of the Chl content of two wheat genotypes grown under three salinity levels. The Chl content was quantified as content per area (Chl area, µg cm-2), concentration per plant (Chl plant, mg plant-1), and SPAD value (Chl SPAD). The performance of spectral reflectance indices (SRIs) with different algorithm forms, partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) in estimating the three units of Chl content was compared. Results show that most indices within each SRI form performed better with Chl area and Chl plant and performed poorly with Chl SPAD. The PLSR models, based on the four forms of SRIs individually or combined, still performed poorly in estimating Chl SPAD, while they exhibited a strong relationship with Chl plant followed by Chl area in both the calibration (Cal.) and validation (Val.) datasets. The SMLR models extracted three to four indices from each SRI form as the most effective indices and explained 73-79%, 80-84%, and 39-43% of the total variability in Chl area, Chl plant, and Chl SPAD, respectively. The performance of the various predictive models of SMLR for predicting Chl content depended on salinity level, genotype, season, and the units of Chl content. In summary, this study indicates that the Chl content measured in the lab and expressed on content (µg cm-2) or concentration (mg plant-1) can be accurately estimated at canopy level using spectral reflectance data.

Designing Low-Cost Capacitive-Based Soil Moisture Sensor and Smart Monitoring Unit Operated by Solar Cells for Greenhouse Irrigation Management.

Precise and quick estimates of soil moisture content for the purpose of irrigation scheduling are fundamentally important. They can be accomplished through the continuous monitoring of moisture content in the root zone area, which can be accomplished through automatic soil moisture sensors. Commercial soil moisture sensors are still expensive to be used by famers, particularly in developing countries, such as Egypt. This research aimed to design and calibrate a locally manufactured low-cost soil moisture sensor attached to a smart monitoring unit operated by Solar Photo Voltaic Cells (SPVC). The designed sensor was evaluated on clay textured soils in both lab and controlled greenhouse environments. The calibration results demonstrated a strong correlation between sensor readings and soil volumetric water content (θV). Higher soil moisture content was associated with decreased sensor output voltage with an average determination coefficient (R2) of 0.967 and a root-mean-square error (RMSE) of 0.014. A sensor-to-sensor variability test was performed yielding a 0.045 coefficient of variation. The results obtained from the real conditions demonstrated that the monitoring system for real-time sensing of soil moisture and environmental conditions inside the greenhouse could be a robust, accurate, and cost-effective tool for irrigation management.

Integration of Radiometric Ground-Based Data and High-Resolution QuickBird Imagery with Multivariate Modeling to Estimate Maize Traits in the Nile Delta of Egypt.

In site-specific management, rapid and accurate identification of crop stress at a large scale is critical. Radiometric ground-based data and satellite imaging with advanced spatial and spectral resolution allow for a deeper understanding of crop stress and the level of stress in a given area. This research aimed to assess the potential of radiometric ground-based data and high-resolution QuickBird satellite imagery to determine the leaf area index (LAI), biomass fresh weight (BFW) and chlorophyll meter (Chlm) of maize across well-irrigated, water stress and salinity stress areas in the Nile Delta of Egypt. Partial least squares regression (PLSR) and multiple linear regression (MLR) were evaluated to estimate the three measured traits based on vegetation spectral indices (vegetation-SRIs) derived from these methods and their combination. Maize field visits were conducted during the summer seasons from 28 to 30 July 2007 to collect ground reference data concurrent with the acquisition of radiometric ground-based measurements and QuickBird satellite imagery. The results showed that the majority of vegetation-SRIs extracted from radiometric ground-based data and high-resolution satellite images were more effective in estimating LAI, BFW, and Chlm. In general, the vegetation-SRIs of radiometric ground-based data showed higher R2 with measured traits compared to the vegetation-SRIs extracted from high-resolution satellite imagery. The coefficient of determination (R2) of the significant relationships between vegetation-SRIs of both methods and three measured traits varied from 0.64 to 0.89. For example, with QuickBird high-resolution satellite images, the relationships of the green normalized difference vegetation index (GNDVI) with LAI and BFW showed the highest R2 of 0.80 and 0.84, respectively. Overall, the ground-based vegetation-SRIs and the satellite-based indices were found to be in good agreement to assess the measured traits of maize. Both the calibration (Cal.) and validation (Val.) models of PLSR and MLR showed the highest performance in predicting the three measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery. For example, validation (Val.) models of PLSR and MLR showed the highest performance in predicting the measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery with R2 (0.91) of both methods for LAI, R2 (0.91-0.93) for BFW respectively, and R2 (0.82) of both methods for Chlm. The models of PLSR and MLR showed approximately the same performance in predicting the three measured traits and no clear difference was found between them and their combinations. In conclusion, the results obtained from this study showed that radiometric ground-based measurements and high spectral resolution remote-sensing imagery have the potential to offer necessary crop monitoring information across well-irrigated, water stress and salinity stress in regions suffering lack of freshwater resources.

Polymer-Peptide Modified Gold Nanorods to Improve Cell Conjugation and Cell labelling for Stem Cells Photoacoustic Imaging.

The use of gold nanorods (GNRs) as a contrast agent in bioimaging and cell tracking has numerous advantages, primarily due to the unique optical properties of gold nanorods which allow for the use of infrared regions when imaging. Owing to their unique geometry, Au NRs exhibit surface plasmon modes in the near-infrared wavelength range, which is ideal for carrying out optical measurements in biological fluids and tissue. Gold nanorod functionalization is essential, since the Cetyltrimethyl ammonium bromide CTAB gold nanorods are toxic, and for further in vitro and in vivo experiments the nanorods should be functionalized to become optically stable and biocompatible. In the present study, gold nanorods with an longitudinal surface plasmon resonance (LSPR) position around 800 nm were synthesized in order to be used for photoacoustic imaging applications for stem cell tracking. The gold nanorods were functionalized using both thiolated poly (ethylene glycol) (PEG) to stabilize the gold nanorods surface and a CALNN-TAT peptide sequence. Both ligands were attached to the gold nanorods through an Au-sulfur bond. CALNN-TAT is known as a cell penetrating peptide which ensures endocytosis of the gold nanorods inside the mesenchymal stem cells of mice (MSCD1). Surface modifications of gold nanorods were achieved using optical spectroscopy (UV-VIS), electron microscopy (TEM), zeta-potential, and FTIR. Gold nanorods were incubated in MSCD1 in order to achieve a cellular uptake that was characterized by a transmission electron microscope (TEM). For photoacoustic imaging, Multi-Spectral Optoacoustic Tomography (MSOT) was used. The results demonstrated good cellular uptake for PEG-CALNN-TAT GNRs and the successful use of modified gold nanorods as both a contrast agent in photoacoustic imaging and as a novel tracking bioimaging technique.

Wuxal amino (Bio stimulant) improved growth and physiological performance of tomato plants under salinity stress through adaptive mechanisms and antioxidant potential.

In the present study, ameliorative capabilities of wuxal amino (bio stimulant) under salt stress has been investigated through adaptive mechanisms and antioxidant potential in tomato plants. In the experiment, two different concentrations (2 cm L-1 and 3 cm L-1) of wuxal amino through foliar application and soil irrigation were applied to the salt (150 mM) treated tomato plants and then morphological traits, photosynthetic pigments, osmolytes, secondary metabolites, oxidative stress and antioxidant enzymes activity were assessed at 60 days after planting. The results revealed that salt stress decreased the growth parameters, photosynthetic pigments, soluble sugars and soluble protein whereas, content of proline, ascorbic acid, total phenols, malondialdehyde, hydrogen peroxide and the activity of antioxidant enzymes activity increased under salt stress. Moreover, Wuxal amino application through foliar or soil to salt stressed plants improved morphological traits, photosynthetic pigments, osmolytes, total phenol and antioxidant enzymes activity. Interestingly, the deleterious impact of salinity on tomato plants were significantly reduced and it can be evident from reduced MDA and H2O2 levels. These responses varied with the mode (foliar or soil) of application of Wuxal amino under different concentrations (2 cm L-1 and 3 cm L-1). It was concluded that application of Wuxal amino (2 cm L-1, foliar) and (3 cm L-1; soil) proved best and could be commercially used as eco-friendly tool for the protection of tomato plants grown under salinity stress.

Corrigendum: Passive Reflectance Sensing and Digital Image Analysis Allows for Assessing the Biomass and Nitrogen Status of Wheat in Early and Late Tillering Stages.

[This corrects the article DOI: 10.3389/fpls.2018.01478.].

Use of Hyperspectral Reflectance Sensing for Assessing Growth and Chlorophyll Content of Spring Wheat Grown under Simulated Saline Field Conditions.

The application of proximal hyperspectral sensing, using simple vegetation indices, offers an easy, fast, and non-destructive approach for assessing various plant variables related to salinity tolerance. Because most existing indices are site- and species-specific, published indices must be further validated when they are applied to other conditions and abiotic stress. This study compared the performance of various published and newly constructed indices, which differ in algorithm forms and wavelength combinations, for remotely assessing the shoot dry weight (SDW) as well as chlorophyll a (Chla), chlorophyll b (Chlb), and chlorophyll a+b (Chlt) content of two wheat genotypes exposed to three salinity levels. Stepwise multiple linear regression (SMLR) was used to extract the most influential indices within each spectral reflectance index (SRI) type. Linear regression based on influential indices was applied to predict plant variables in distinct conditions (genotypes, salinity levels, and seasons). The results show that salinity levels, genotypes, and their interaction had significant effects (p ≤ 0.05 and 0.01) on all plant variables and nearly all indices. Almost all indices within each SRI type performed favorably in estimating the plant variables under both salinity levels (6.0 and 12.0 dS m-1) and for the salt-sensitive genotype Sakha 61. The most effective indices extracted from each SRI type by SMLR explained 60%-81% of the total variability in four plant variables. The various predictive models provided a more accurate estimation of Chla and Chlt content than of SDW and Chlb under both salinity levels. They also provided a more accurate estimation of SDW than of Chl content for salt-tolerant genotype Sakha 93, exhibited strong performance for predicting the four variables for Sakha 61, and failed to predict any variables under control and Chlb for Sakha 93. The overall results indicate that the simple form of indices can be used in practice to remotely assess the growth and chlorophyll content of distinct wheat genotypes under saline field conditions.

Potential of Hyperspectral and Thermal Proximal Sensing for Estimating Growth Performance and Yield of Soybean Exposed to Different Drip Irrigation Regimes Under Arid Conditions.

Proximal hyperspectral sensing tools could complement and perhaps replace destructive traditional methods for accurate estimation and monitoring of various morpho-physiological plant indicators. In this study, we assessed the potential of thermal imaging (TI) criteria and spectral reflectance indices (SRIs) to monitor different vegetative growth traits (biomass fresh weight, biomass dry weight, and canopy water mass) and seed yield (SY) of soybean exposed to 100%, 75%, and 50% of estimated crop evapotranspiration (ETc). These different plant traits were evaluated and related to TI criteria and SRIs at the beginning bloom (R1) and full seed (R6) growth stages. Results showed that all plant traits, TI criteria, and SRIs presented significant variations (p < 0.05) among irrigation regimes at both growth stages. The performance of TI criteria and SRIs for assessment of vegetative growth traits and SY fluctuated when relationships were analyzed for each irrigation regime or growth stage separately or when the data of both conditions were combined together. TI criteria and SRIs exhibited a moderate to strong relationship with vegetative growth traits when data from different irrigation regimes were pooled together at each growth stage or vice versa. The R6 and R1 growth stages are suitable for assessing SY under full (100% ETc) and severe (50% ETc) irrigation regimes, respectively, using SRIs. The overall results indicate that the usefulness of the TI and SRIs for assessment of growth, yield, and water status of soybean under arid conditions is limited to the growth stage, the irrigation level, and the combination between them.

Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines Under Two Contrasting Irrigation Regimes.

The incorporation of nondestructive and cost-effective tools in genetic drought studies in combination with reliable indirect screening criteria that exhibit high heritability and genetic correlations will be critical for addressing the water deficit challenges of the agricultural sector under arid conditions and ensuring the success of genotype development. In this study, the proximal spectral reflectance data were exploited to assess three destructive agronomic parameters [dry weight (DW) and water content (WC) of the aboveground biomass and grain yield (GY)] in 30 recombinant F7 and F8 inbred lines (RILs) growing under full (FL) and limited (LM) irrigation regimes. The utility of different groups of spectral reflectance indices (SRIs) as an indirect assessment tool was tested based on heritability and genetic correlations. The performance of the SRIs and different models of partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) in estimating the destructive parameters was considered. Generally, all groups of SRIs, as well as different models of PLSR and SMLR, generated better estimations for destructive parameters under LM and combined FL+LM than under FL. Even though most of the SRIs exhibited a low association with destructive parameters under FL, they exhibited moderate to high genetic correlations and also had high heritability. The SRIs based on near-infrared (NIR)/visible (VIS) and NIR/NIR, especially those developed in this study, spectral band intervals extracted within VIS, red edge, and NIR spectral range, or individual effective wavelengths relevant to green, red, red edge, and middle NIR spectral region, were found to be more effective in estimating the destructive parameters under all conditions. Five models of SMLR and PLSR for each condition explained most of the variation in the three destructive parameters among genotypes. These models explained 42% to 46%, 19% to 30%, and 39% to 46% of the variation in DW, WC, and GY among genotypes under FL, 69% to 72%, 59% to 61%, and 77% to 81% under LM, and 71% to 75%, 61% to 71%, and 74% to 78% under FL+LM, respectively. Overall, these results confirmed that application of hyperspectral reflectance sensing in breeding programs is not only important for evaluating a sufficient number of genotypes in an expeditious and cost-effective manner but also could be exploited to develop indirect breeding traits that aid in accelerating the development of genotypes for application under adverse environmental conditions.

Correction: Combining biophysical parameters, spectral indices and multivariate hyperspectral models for estimating yield and water productivity of spring wheat across different agronomic practices.

[This corrects the article DOI: 10.1371/journal.pone.0212294.].

Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis.

The timely estimation of growth and photosynthetic-related traits in an easy and nondestructive manner using hyperspectral data will become imperative for addressing the challenges of environmental stresses inherent to the agricultural sector in arid conditions. However, the handling and analysis of these data by exploiting the full spectrum remains the determining factor for refining the estimation of crop variables. The main objective of this study was to estimate growth and traits underpinning photosynthetic efficiency of two wheat cultivars grown under simulated saline field conditions and exposed to three salinity levels using hyperspectral reflectance information from 350-2500 nm obtained at two years. Partial least squares regression (PLSR) based on the full spectrum was applied to develop predictive models for estimating the measured parameters in different conditions (salinity levels, cultivars, and years). Variable importance in projection (VIP) of PLSR in combination with multiple linear regression (MLR) was implemented to identify important waveband regions and influential wavelengths related to the measured parameters. The results showed that the PLSR models exhibited moderate to high coefficients of determination (R2) in both the calibration and validation datasets (0.30-0.95), but that this range of R2 values depended on parameters and conditions. The PLSR models based on the full spectrum accurately and robustly predicted three of four parameters across all conditions. Based on the combination of PLSR-VIP and MLR analysis, the wavelengths selected within the visible (VIS), red-edge, and middle near-infrared (NIR) wavebands were the most sensitive to all parameters in all conditions, whereas those selected within the shortwave infrared (SWIR) waveband were effective for some parameters in particular conditions. Overall, these results indicated that the PLSR analysis and band selection techniques can offer a rapid and nondestructive alternative approach to accurately estimate growth- and photosynthetic-related trait responses to salinity stress.

Performance of optimized hyperspectral reflectance indices and partial least squares regression for estimating the chlorophyll fluorescence and grain yield of wheat grown in simulated saline field conditions.

To overcome the salinity threats to crop production in arid conditions, wheat cultivars should be developed with better performance with regard to key physiological traits. Although different chlorophyll fluorescence (ChlF) parameters, such as maximum quantum PSII photochemical efficiency (Fv/Fm), quantum yield of PSII (ΦPSII), and non-photochemical quenching (NPQ) have been proven to be key physiological traits to improve salt tolerance, their evaluation is time-consuming. In this study, hyperspectral canopy reflectance was used to assess ChlF parameters and grain yield (GY) of two wheat cultivars growing in simulated saline field conditions and exposed to three salinity levels (control, 6.0 dS m-1, and 12.0 dS m-1). Different spectral reflectance indices (SRIs) were formulated as ratios based on contour maps and tested for their relationship with ChlF parameters. The performance of individual SRIs and partial least squares regression (PLSR) models based on ChlF parameters, all examined SRIs, or data fusion of combined ChlF and SRIs to estimate the GY was considered. All examined SRIs failed to assess ΦPSII and NPQ under control condition, but most of them showed a moderate to strong relationship with both parameters under the salinity levels of 6.0 and 12.0 dS m-1. The examined SRIs showed a moderate and strong relationship with Fv/Fm under conditions of 6.0 and 12.0 dS m-1, respectively. Most SRIs correlated better with the three ChlF parameters for the salt-sensitive cultivar Sakha 61 than for the salt-tolerant cultivar Sakha 93. Several SRIs exhibited strong relationships with GY under the salinity levels of 6.0 and 12.0 dS m-1 and for both cultivars. Overall, the PLSR models exhibited additional improvements for estimating and predicting GY in both calibration and validation datasets over that using individual SRIs. The PLSR model based on data fusion was the best model to accurately estimate GY in the validation model even under control conditions. This study, of a type rarely conducted in simulated saline field conditions, indicates that the ChlF parameters could be linked to hyperspectral reflectance data for the rapid and non-destructive assessment of photosynthetic status and prediction of wheat production under salt stress field conditions.

Combining biophysical parameters, spectral indices and multivariate hyperspectral models for estimating yield and water productivity of spring wheat across different agronomic practices.

Manipulating plant densities under different irrigation rates can have a significant impact on grain yield and water use efficiency by exerting positive or negative effects on ET. Whereas traditional spectral reflectance indices (SRIs) have been used to assess biophysical parameters and yield, the potential of multivariate models has little been investigated to estimate these parameters under multiple agronomic practices. Therefore, both simple indices and multivariate models (partial least square regression (PLSR) and support vector machines (SVR)) obtained from hyperspectral reflectance data were compared for their applicability for assessing the biophysical parameters in a field experiment involving different combinations of three irrigation rates (1.00, 0.75, and 0.50 ET) and five plant densities (D1: 150, D2: 250, D3: 350, D4: 450, and D5: 550 seeds m-2) in order to improve productivity and water use efficiency of wheat. Results show that the highest values for green leaf area, aboveground biomass, and grain yield were obtained from the combination of D3 or D4 with 1.00 ET, while the combination of 0.75 ET and D3 was the best treatment for achieving the highest values for water use efficiency. Wheat yield response factor (ky) was acceptable when the 0.75 ET was combined with D2, D3, or D4 or when the 0.50 ET was combined with D2 or D3, as the ky values of these combinations were less than or around one. The production function indicated that about 75% grain yield variation could be attributed to the variation in seasonal ET. Results also show that the performance of the SRIs fluctuated when regressions were analyzed for each irrigation rate or plant density specifically, or when the data of all irrigation rates or plant densities were combined. Most of the SRIs failed to assess biophysical parameters under specific irrigation rates and some specific plant densities, but performance improved substantially for combined data of irrigation rates and some specific plant densities. PLSR and SVR produced more accurate estimations of biophysical parameters than SRIs under specific irrigation rates and plant densities. In conclusion, hyperspectral data are useful for predicting and monitoring yield and water productivity of spring wheat across multiple agronomic practices.

Passive Reflectance Sensing and Digital Image Analysis Allows for Assessing the Biomass and Nitrogen Status of Wheat in Early and Late Tillering Stages.

Proximal remote sensing systems depending on spectral reflectance measurements and image analysis can acquire timely information to make real-time management decisions compared to laborious destructive measurements. There is a need to make nitrogen management decisions at early development stages of cereals when the first top-dressing is made. However, there is insufficient information available about the possibility of detecting differences in the biomass or the nitrogen status of cereals at early development stages and even less comparing its relationship to destructively obtained information. The performance of hyperspectral passive reflectance sensing and digital image analysis was tested in a 2-year study to assess the nitrogen uptake and nitrogen concentration, as well as the biomass fresh and dry weight at early and late tillering stages of wheat from BBCH 19 to 30. Wheat plants were subjected to different levels of nitrogen fertilizer applications and differences in biomass, and the nitrogen status was further created by varying the seeding rate. To analyze the spectral and digital imaging data simple linear regression and partial least squares regression (PLSR) models were used. The green pixel digital analysis, spectral reflectance indices and PLSR of spectral reflectance from 400 to 1000 nm were strongly related to the nitrogen uptake and the biomass fresh and dry weights at individual measurements and for the combined dataset at the early crop development stages. Relationships between green pixels, spectral reflectance indices and PLSR with the biomass and nitrogen status parameters reached coefficients of determination up to 0.95∗∗ through the individual measurements and the combined data set. Reflectance-based spectral sensing compared to digital image analysis allows detecting differences in the biomass and nitrogen status already at early growth stages in the tillering phase. Spectral reflectance indices are probably more robust and can more easily be applied compared to PLSR models. This might pave the way for more informed management decisions and potentially lead to improved nitrogen fertilizer management at early development stages.

The correlation between radiographic and surface topography assessments in three plane pelvic parameters.

BACKGROUND: Noninvasive rasterstereography has been reported as a helpful tool for assessing pelvic parameters. However, the validation and reproducibility of this tool are still questionable. OBJECTIVE: To investigate the correlation between video rasterstereography device (VRD) and X-ray photography in terms of validity and reproducibility (inter- and intra-examiner reliability) in pelvic parameters. METHODS: Thirty male and female healthy subjects with a mean age (26.9 ± 4.9 years) participated in this study. Subjects were examined by VRD for three pelvic parameters (pelvic torsion, pelvic tilt, and pelvic inclination). Measurements were conducted by three different examiners. Subjects were examined by X-ray radiography for the same pelvic parameters as well. Pearson correlation coefficient (r) was used to examine the validity and Intra-class correlation coefficient (ICC) was used to check intra- and inter-examiner reliability. RESULTS: For pelvic torsion, tilt, and inclination the validity levels of VRD were 0.867, 0.996, and 0.930 (P < 0.05), respectively; while the intra- and inter-examiner reliability coefficients were 0.999, 0.999, and 0.998 and 0.990, 0.997, and 0.989, respectively. CONCLUSION: The results of this study revealed that the VRD has both high validity and reliability in assessing the selected three pelvic parameters that reflect the three fundamental planes of movement in healthy subjects. Further studies using VRD are recommended to assess low back pain-associated pelvic parameters.

Development of a diurnal dehydration index for spring barley phenotyping.

Spectral and thermal assessments may enable the precise, high-throughput and low-cost characterisation of traits linked to drought tolerance. However, spectral and thermal measurements of the canopy water status are influenced by the crops' soil coverage, the size of the biomass and other properties such as the leaf angle distribution. The aim of this study was to develop a referenced spectral method that would be minimally influenced by potentially perturbing factors for retrieving the water status of differing cultivars. Sixteen spring barley cultivars were grown in field trials under imposed drought stress, natural drought stress and irrigated conditions. The relative leaf water content of barley plants declines diurnally from pre-dawn until the afternoon, and other plant traits such as the biomass change little throughout the day. As an indicator of the current drought stress, pre-dawn and afternoon values of the relative leaf water content were assessed spectrally. Diurnal changes in reflectance are only slightly influenced by other perturbing factors. A new spectral index (diurnal dehydration index) was developed by using the wavelengths 730 and 457nm collected from an active spectrometer. This index allowed the differentiation of the drought tolerance of barley plants. The diurnal dehydration index was significantly related to final biomass, grain yield and harvest index and significantly different between cultivars. Compared with other indices, the diurnal dehydration index offered a higher stability in retrieving the water status of barley plants. Due to its diurnal assessment, the index was barely influenced by the differences in cultivars biomass at the time of measurement. It may represent a valuable tool for assessing the water status or drought tolerance in breeding nurseries.

Can changes in leaf water potential be assessed spectrally?

Leaf water potential (LWP) is an important indicator of plant water status. However, its determination via classical pressure-chamber measurements is tedious and time-consuming. Moreover, such methods cannot easily account for rapid changes in this parameter arising from changes in environmental conditions. Spectrometric measurements, by contrast, have the potential for fast and non-destructive measurements of plant water status, but are not unproblematic. Spectral characteristics of plants vary across plant development stages and are also influenced by environmental factors. Thus, it remains unclear whether changes in leaf water potential per se can reliably be detected spectrometrically or whether such measurements also reflect autocorrelated changes in the leaf water content (LWC) or the aerial plant biomass. We tested the accuracy of spectrometric measurements in this context under controlled climate chamber conditions in series of six experiments that minimised perturbing influences but allowed for significant changes in the LWP. Short-term exposure of dense stands of plants to increasing or decreasing artificial light intensities in a growth chamber more markedly decreased LWP than LWC in both wheat and maize. Significant relationships (R2-values 0.74-0.92) between LWP and new spectral indices ((R940/R960)/NDVI; R940/R960) were detected with or without significant changes in LWC of both crop species. The exact relationships found, however, were influenced strongly by the date of measurement or water stress induced. Thus, global spectral relationships measuring LWP probably cannot be established across plant development stages. Even so, spectrometric measurements supplemented by a reduced calibration dataset from pressure chamber measurements might still prove to be a fast and accurate method for screening large numbers of diverse lines.