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Studying the physiological mechanisms underlying the traits associated with salt tolerance in genotypes could lead to the discovery of new genetic resources for salt tolerance. In this study, the mechanisms of salt tolerance were evaluated, based on ionic, physiological, and agronomic traits in four varieties that differ in their salt tolerance and in 18 F8 recombinant inbred lines (RILs) grown in real field conditions. The salt tolerance of plant materials was assessed under both normal (3.5 mM NaCl) and high salinity stress (150 mM NaCl) conditions for two consecutive years. Different growth and physiological traits were assessed 75 days after sowing, while ion contents in the shoots, grain yield, and its components were determined at the maturity stage. Multivariate analysis was used to conduct a comprehensive evaluation of salt tolerance across various genotypes and traits. The ANOVA results showed significant differences (p ≤ 0.05 and 0.001) among salinity, genotypes, and their interactions for all ionic and agro-physiological traits, with a few exceptions. Salinity stress resulted in a considerable increase in Na+ content and canopy temperature (CT), with a simultaneous decrease of 11.3% to 94.5% in other ionic and agro-physiological traits compared to the control treatment. However, the salt-tolerant genotypes showed minimal increases in Na+ content and CT, as well as decreases in other ionic and agro-physiological traits when compared to salt-sensitive genotypes under salinity stress. All ionic and agro-physiological traits exhibited strong correlations with each other under salinity stress, but these correlations were weak or insignificant under control conditions. The principal component analysis identified Na+ and CT as negative indicators and other ionic and agro-physiological traits as positive indicators for salt tolerance under salinity stress. The negative indicators were strongly linked to salt-sensitive genotypes, while the positive indicators were closely associated with salt-tolerant genotypes. Heatmap clustering, using multiple traits, successfully differentiated the salt-tolerant genotypes from the salt-sensitive ones. The salt-tolerant group showed a significant reduction in Na+ content by 36.9%, in CT by 10.0%, and in HI by 16.7%, along with an increase of 6.3–51.4% in other ionic and agro-physiological traits compared to the salt-sensitive group. In conclusion, the mechanisms associated with Na+ exclusion and high K+/Na+ and Ca2+/Na+ ratios, as well as chlorophyll and relative water content, along with low CT, resulted in significant improvements in growth and yield under salinity stress conditions. Given that the effectiveness of various ionic and agro-physiological traits in evaluating salt tolerance in wheat has been proven in real field conditions, these traits will play a key role in the development of salt-tolerant wheat genotypes.

Developing new bread wheat varieties that can be successfully grown in saline conditions has become a pressing task for plant breeders. High-throughput phenotyping tools are crucial for this task. Proximal remote sensing is gaining popularity in breeding programs as a quick, cost-effective, and non-invasive tool to assess canopy structure and physiological traits in large genetic pools. Limited research has been conducted on the effectiveness of combining RGB and thermal imaging to assess the salt tolerance of different wheat genotypes. This study aimed to evaluate the effectiveness of combining several indices derived from thermal infrared and RGB images with artificial neural networks (ANNs) for assessing relative water content (RWC), chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (Chlt), and plant dry weight (PDW) of 18 recombinant inbred lines (RILs) and their 3 parents irrigated with saline water (150 mM NaCl). The results showed significant differences in various traits and indices among the tested genotypes. The normalized relative canopy temperature (NRCT) index exhibited strong correlations with RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.50 to 0.73, 0.53 to 0.76, 0.68 to 0.84, 0.68 to 0.84, and 0.52 to 0.76, respectively. Additionally, there was a strong relationship between several RGB indices and measured traits, with the highest R2 values reaching up to 0.70. The visible atmospherically resistant index (VARI), a popular index derived from RGB imaging, showed significant correlations with NRCT, RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.49 to 0.62 across two seasons. The different ANNs models demonstrated high predictive accuracy for NRCT and other measured traits, with R2 values ranging from 0.62 to 0.90 in the training dataset and from 0.46 to 0.68 in the cross-validation dataset. Thus, our study shows that integrating high-throughput digital image tools with ANN models can efficiently and non-invasively assess the salt tolerance of a large number of wheat genotypes in breeding programs.

A 1 × 4 planar fractal array antenna is designed and presented for millimeter-wave 5G applications. The array’s top side is made up of a rhombus-inscribed circular ring fractal patch radiator, while the backside is made up of a square notch-loaded partial ground plane. To achieve high gain, a 1 × 4 corporate feeding network is used to excite the array elements. The designed array has an overall size of 28 × 17.75 mm2. From simulations as well as experimental results, it is observed that the designed array offers a wide impedance bandwidth in the frequency range of 22.8–29.2 GHz. Furthermore, a peak gain of 10.7 dBi with a radiation efficiency of >95% is observed in the operating bandwidth.

Successfully evaluating and improving the salt tolerance of genotypes requires an appropriate analysis tool to allow simultaneous analysis of multiple traits and to facilitate the ranking of genotypes across different growth stages and salinity levels. In this study, we evaluate the salt tolerance of 56 recombinant inbred lines (RILs) in the presence of salt-tolerant and salt-sensitive control genotypes using multivariate analysis of plant dry weight, measured at 75 (PDW-75) and 90 (PDW-90) days from sowing, biological yield (BY), grain yield (GY), and their salt tolerance indices (STIs). All RILs and genotypes were evaluated under the control and 15 dS m−1 for two consecutive years (2019/2020 and 2020/2021). Results showed significant main effects of salinity and genotype as well as their interactions on four plant traits. Significant genotypic differences were also found for all calculated STIs. STIs exhibited moderate to strong relationships with the four plant traits when measured under either the control or salinity conditions and between each other. The principal component analysis (PCA) showed that the most variation among all analyzed variables was explained by the first two PCs, with the PC1 and PC2 explained at 61.8–71.8% and at 28.0–38.2% of the total variation, respectively. The PC1 had positive and strong correlations with the four plant traits measured under salinity conditions and STI, YI, REI, SWPI, MRPI, MPI, GMPI, and HMPI. The PC2 had strong correlations with BY and GY measured under the control conditions and SSI, TOL, RSE, and YSI. The PC1 was able to identify the salt-tolerant genotypes, while the PC2 was able to isolate the salt-sensitive ones. Cluster analysis based on multiple traits organized 64 genotypes into four groups varied from salt-tolerant to salt-sensitive genotypes, with the salt-tolerant group attaining higher value for plant traits under salinity conditions and the STIs related to the PC1. In conclusion, the use of multivariate analysis together with the STIs that evaluated the performance of genotypes under contrasting environmental conditions will help breeders to distinguish salt-tolerant genotypes from salt-sensitive ones, even at the early growth stages of plant development.

Assessment of the salt tolerance of a large genotype collection at the early growth stages may assist in the fast-tracking improvement of salt-tolerant barley genotypes in breeding programs. This study aimed to investigate the ability of traits related to seed germination ability and seedling growth performance with helping of nine sequence-related amplified polymorphism (SRAP) markers to detect the salt tolerance of 70 barley genotypes during the early growth stages. The different genotypes were exposed to three salt concentrations (0, 100, and 200 mM NaCl) and evaluated for salt tolerance by looking at germination percentage, germination index, and mean germination time during eight days as well as the lengths and weights of seedling shoot and root after 21 days from sowing. The results showed that genotypic variations in germination ability and seedling growth performance obviously appeared under 200 and 100 mM NaCl, respectively. The germination traits exhibited a strong correlation among themselves, whereas they had a poor correlation with seedling traits. A strong and positive correlation was only observed for shoot fresh weight with shoot length and root fresh weight under salinity conditions. Principal component analysis revealed that the first two components, which explained 53% of the total variability, succeeded to identify the genotypes with high salt tolerance during only one stage (germination or seedling stage) and both stages. Cluster analysis based on the stress tolerance index of germination and seedling traits grouped 70 genotypes into four key clusters, with genotypes grouped in cluster 1 and cluster 2 being salt tolerant during the germination stage and moderately tolerant during the seedling stage; the opposite was found with the genotypes grouped in cluster 4. According to Ward’s method, the salt tolerance of genotypes that ranked as most salt-tolerant (T) or salt-sensitive (S) remained almost unchanged during germination and seedling stages. In contrast, a change in salt tolerance with both stages was found for the genotypes that ranked as moderately salt-tolerant (MT) and salt-sensitive (MS) genotypes. The nine SRAP markers divided the tested genotypes into two distinct clusters, with clusters B had the most T and MT genotypes. Finally, using appropriate statistical methods presented in this study with SRAP markers will be useful for assessing the salt tolerance of a large number of barley genotypes and selecting the genotypes tolerant of and sensitive to salinity at the early growth stage.

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.

An area of growing interest in wheat-breeding programs for abiotic stresses is the accurate and expeditious phenotyping of large genotype collections using nondestructive hyperspectral sensing tools. The main goal of this study was to use data from canopy spectral signatures (CSS) in the full-spectrum range (400–2500 nm) to estimate and predict the plant biomass dry weight at booting (BDW-BT) and anthesis (BDW-AN) growth stages, and biological yield (BY) of 64 spring wheat germplasms exposed to 150 mM NaCl using 13 spectral reflectance indices (SRIs, consisting of seven vegetation-related SRIs and six water-related SRIs) and partial least squares regression (PLSR). SRI and PLSR performance in estimating plant traits was evaluated during two years at BT, AN, and early milk grain (EMG) growth stages. Results showed significant genotypic differences between the three traits and SRIs, with highly significant two-way and three-way interactions between genotypes, years, and growth stages for all SRIs. Genotypic differences in CSS and the relationships between the three traits and a single wavelength over the full-spectrum range depended on the growth stage. Water-related SRIs were more strongly correlated with the three traits compared with vegetation-related SRIs at the BT stage; the opposite was found at the EMG stage. Both types of SRIs exhibited comparable associations with the three traits at the AN stage. Principal component analysis indicated that it is possible to assess plant biomass variations at an early stage (BT) through published and modified SRIs. SRIs coupled with PLSR models at the BT stage exhibited good prediction capacity of BDW-BT (57%), BDW-AN (82%), and BY (55%). Overall, results demonstrated that the integration of SRIs and multivariate models may present a feasible tool for plant breeders to increase the efficiency of the evaluation process and to improve the genetics for salt tolerance in wheat-breeding programs.

The incorporation of stress tolerance indices (STIs) with the early estimation of grain yield (GY) in an expeditious and nondestructive manner can enable breeders for ensuring the success of genotype development for a wide range of environmental conditions. In this study, the relative performance of GY for sixty-four spring wheat germplasm under the control and 15.0 dS m−1 NaCl were compared through different STIs, and the ability of a hyperspectral reflectance tool for the early estimation of GY and STIs was assessed using twenty spectral reflectance indices (SRIs; 10 vegetation SRIs and 10 water SRIs). The results showed that salinity treatments, genotypes, and their interactions had significant effects on the GY and nearly all SRIs. Significant genotypic variations were also observed for all STIs. Based on the GY under the control (GYc) and salinity (GYs) conditions and all STIs, the tested genotypes were classified into three salinity tolerance groups (salt-tolerant, salt-sensitive, and moderately salt-tolerant groups). Most vegetation and water SRIs showed strong relationships with the GYc, stress tolerance index (STI), and geometric mean productivity (GMP); moderate relationships with GYs and sometimes with the tolerance index (TOL); and weak relationships with the yield stability index (YSI) and stress susceptibility index (SSI). Obvious differences in the spectral reflectance curves were found among the three salinity tolerance groups under the control and salinity conditions. Stepwise multiple linear regressions identified three SRIs from each vegetation and water SRI as the most influential indices that contributed the most variation in the GY. These SRIs were much more effective in estimating the GYc (R2 = 0.64 − 0.79) than GYs (R2 = 0.38 − 0.47). They also provided a much accurate estimation of the GYc and GYs for the moderately salt-tolerant genotype group; YSI, SSI, and TOL for the salt-sensitive genotypes group; and STI and GMP for all the three salinity tolerance groups. Overall, the results of this study highlight the potential of using a hyperspectral reflectance tool in breeding programs for phenotyping a sufficient number of genotypes under a wide range of environmental conditions in a cost-effective, noninvasive, and expeditious manner. This will aid in accelerating the development of genotypes for salinity conditions in breeding programs.

Now a days a country cannot become economically strong until and unless it has enough electrical power to fulfil industrial and domestic needs. Electrical power being the pillar of any country’s economy, needs to be used in an efficient way. The same step is taken here by proposing a new system for energy distribution from substation to consumer houses, also it monitors the consumer consumption and record data. Unlike traditional manual Electrical systems, pervasive electricity distribution system (PEDS) introduces a fresh perspective to monitor the feeder line status at distribution and consumer level. In this system an effort is taken to address the issues of electricity theft, manual billing, online monitoring of electrical distribution system and automatic control of electrical distribution points. The project is designed using microcontroller and different sensors, its GUI is designed in Labview software.

In the petroleum industry, the researchers have developed a new technique called enhanced oil recovery to recover the remaining oil in reservoirs. Some reservoirs are very complex and require advanced enhanced oil recovery (EOR) techniques containing new materials and additives in order to produce maximum oil in economic and environmental friendly manners. In this work, the effects of nanosuspensions (KY-200) and polymer gel HPAM (854) on oil recovery and water cut were studied in the view of EOR techniques and their results were compared. The mechanism of nanosuspensions transportation through the sand pack was also discussed. The adopted methodology involved the preparation of gel, viscosity test, and core flooding experiments. The optimum concentration of nanosuspensions after viscosity tests was used for displacement experiments and 3 wt % concentration of nanosuspensions amplified the oil recovery. In addition, high concentration leads to more agglomeration; thus, high core plugging takes place and diverts the fluid flow towards unswept zones to push more oil to produce and decrease the water cut. Experimental results indicate that nanosuspensions have the ability to plug the thief zones of water channeling and can divert the fluid flow towards unswept zones to recover the remaining oil from the reservoir excessively rather than the normal polymer gel flooding. The injection pressure was observed higher during nanosuspension injection than polymer gel injection. The oil recovery was achieved by about 41.04% from nanosuspensions, that is, 14.09% higher than polymer gel. Further investigations are required in the field of nanoparticles applications in enhanced oil recovery to meet the world's energy demands.

Advances in proximal hyperspectral sensing tools, chemometric techniques, and data-driven modeling have enhanced precision irrigation management by facilitating the monitoring of several plant traits. This study investigated the performance of remote sensing indices derived from thermal and red-green-blue (RGB) images combined with stepwise multiple linear regression (SMLR) and an integrated adaptive neuro-fuzzy inference system with a genetic algorithm (ANFIS-GA) for monitoring the biomass fresh weight (BFW), biomass dry weight (BDW), biomass water content (BWC), and total tuber yield (TTY) of two potato varieties under 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Results showed that the plant traits and indices varied significantly between the three irrigation regimes. Furthermore, all of the indices exhibited strong relationships with BFW, CWC, and TTY (R2 = 0.80–0.92) and moderate to weak relationships with BDW (R2 = 0.25–0.65) when considered for each variety across the irrigation regimes, for each season across the varieties and irrigation regimes, and across all data combined, but none of the indices successfully assessed any of the plant traits when considered for each irrigation regime across the two varieties. The SMLR and ANFIS-GA models gave the best predictions for the four plant traits in the calibration and testing stages, with the exception of the SMLR testing model for BDW. Thus, the use of thermal and RGB imaging indices with ANFIS-GA models could be a practical tool for managing the growth and production of potato crops under deficit irrigation regimes.

Simultaneous and timely assessment of growth and water status-related plant traits is critical for precision irrigation management in arid regions. Here, we used proximal hyperspectral sensing tools to estimate biomass fresh weight (BFW), biomass dry weight (BDW), canopy water content (CWC), and total tuber yield (TTY) of two potato varieties irrigated with 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Plant traits were assessed remotely using published and newly constructed vegetation and water spectral reflectance indices (SRIs). We integrated genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) models to predict the measured traits based on all SRIs. The different plant traits and SRIs varied significantly (p < 0.05) between the three irrigation regimes for the two varieties. The values of plant traits and majority SRIs showed a continuous decrease from the 100% ETc to the 50% ETc. Water-SRIs performed better than vegetation-SRIs for estimating the four plant traits. Almost all indices of the two SRI types had a weak relationship with the four plant traits (R2 = 0.00–0.37) under each irrigation regime. However, the majority of vegetation-SRIs and all water-SRIs showed strong relationships with BFW, CWC, and TTY (R2 ≥ 0.65) and moderate relationships with BDW (R2 ≥ 0.40) when the data of all irrigation regimes and varieties were analyzed together for each growing season or the data of all irrigation regimes, varieties, and seasons were combined together. The ANFIS-GA model predicted plant traits with satisfactory accuracy in both calibration (R2 = 1.0) and testing (R2 = 0.72–0.97) modes. The results indicate that SRI-based ANFIS models can improve plant trait estimation. This analysis also confirmed the benefits of applying GA to ANFIS to estimate plant responses to different growth 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.

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.

Field-based trials and genotype evaluation until yielding stage are two important steps in improving the salt tolerance of crop genotypes and identifying what parameters can be strong candidates for the better understanding of salt tolerance mechanisms in different genotypes. In this study, the salt tolerance of 18 bread wheat genotypes was evaluated under natural saline field conditions and at three saline irrigation levels (5.25, 8.35, and 11.12 dS m−1) extracted from wells. Multidimensional evaluation for salt tolerance of these genotypes was done using a set of agronomic and physio-biochemical attributes. Based on yield index under three salinity levels, the genotypes were classified into four groups ranging from salt-tolerant to salt-sensitive genotypes. The salt-tolerant genotypes exhibited values of total chlorophyll, gas exchange (net photosynthetic rate, transpiration rate, and stomatal conductance), water relation (relative water content and membrane stability index), nonenzymatic osmolytes (soluble sugar, free proline, and ascorbic acid), antioxidant enzyme activities (superoxide dismutase, catalase, and peroxidase), K+ content, and K+/Na+ ratio that were greater than those of salt-sensitive genotypes. Additionally, the salt-tolerant genotypes consistently exhibited good control of Na+ and Cl− levels and maintained lower contents of malondialdehyde and electrolyte leakage under high salinity level, compared with the salt-sensitive genotypes. Several physio-biochemical parameters showed highly positive associations with grain yield and its components, whereas negative association was observed in other parameters. Accordingly, these physio-biochemical parameters can be used as individual or complementary screening criteria for evaluating salt tolerance and improvement of bread wheat genotypes under natural saline field conditions.

Progress in high-throughput tools has enabled plant breeders to increase the rate of genetic gain through multidimensional assessment of previously intractable traits in a fast and nondestructive manner. This study investigates the potential use of spectral reflectance indices (SRIs; 15 vegetation-SRIs; 15 water-SRIs) as alternative selection tools for destructively measured traits in wheat breeding programs. The genetic variability, heritability (h2), genetic gain (GG), and expected genetic advances (GA) of these indices were compared with those of destructively measured traits in 43 F7-8 recombinant inbred lines (RILs) grown under limited water conditions. The performance of SRIs to estimate the destructively measured traits directly was also evaluated using the partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) models. Most vegetation-SRIs exhibited high genotypic variation, similar to the measured traits, and phenotypic correlations with these traits, compared with the water-SRIs. Most vegetation-SRIs presented comparable values for h2 (>60%) and GG (>20%) as intermediate traits, while about half of water-SRIs exhibited a high h2 (>60%), but low GG (2 in calibration (0.53–0.75) and validation (0.46–0.72) datasets, with strong relationships between observed and predicted values of measured traits. The SMLR models identified four and three SRIs from vegetation-SRIs and water-SRIs, respectively, to explain 63–86% of the total variability in measured traits among genotypes. These results demonstrated that vegetation-SRIs can be used individually or combined with water-SRIs as alternative breeding tools to increase genetic gains and selection accuracy in spring wheat breeding.

Bio-activated organic fertilizers (BOZ) were produced by enriching the zinc oxide (ZnO)-orange peel waste composite with Zn solubilizing bacteria (ZSB: Bacillus sp. AZ6) in various formulations (BOZ1 (9:1), BOZ2 (8:2), BOZ3 (7:3) and BOZ4 (6:4)). The produced BOZs, along with ZnO, ZnSO4, ZSB were applied to maize crop (Zea mays L.) under field conditions in two different cropping season and the growth, yield, physiology, plant Zn contents and quality of maize were investigated. Results revealed significant variation in the aforementioned parameters with the applied amendments. The BOZ4 performed outclass by exhibiting the highest plant growth, yield, physiology, Zn contents, and quality. On average, an increase of 53%, 49%, 19%, 22%, 10%, 4%, and 30% in plant height was noticed with BOZ4 application over control, ZnO, ZnSO4, BOZ1, BOZ2, BOZ3, and ZSB, respectively. BOZ4 enhanced the dry shoot-biomass 46% than control. Likewise, the photosynthetic rate, transpiration rate, stomatal conductance, chlorophyll contents, carotenoids, and carbonic anhydrase activity were increased by 47%, 42%, 45%, 57%, 17%, and 44%, respectively, under BOZ4 over control in both cropping seasons. However, BOZ4 reduced the electrolyte leakage by 38% as compared to control in both cropping seasons. BOZ4 increased the Zn contents of grain and shoot by 46% and 52%, respectively, while reduced the phytate contents by 73% as compared to control. Application of BOZ4 revealed highest average fat (4.79%), crude protein (12.86%), dry matter (92.03%), fiber (2.87%), gluten (11.925%) and mineral (1.53%) contents, as compared to control. In general, the impact of cropping seasons on maize growth, yield, physiology, Zn contents, and quality were non-significant (with few exceptions). Thus, bio-activation of ZnO with ZSB could serve as an efficient and economical strategy for boosting up the growth, yield, physiological, and quality parameters of maize under field conditions.

Imidacloprid (IC) is a systemic insecticide related to the tobacco toxin nicotine. IC is a toxic substance frequently used into combat insects, rodents and plants pests and other creatures that can pose problems for agriculture. We, therefore, planned this study to assess risk factors, biochemical and histological alterations associated with hepatotoxicity and nephrotoxicity. Forty-eight adult male albino mice were divided into four groups of 12 animals each. All the animals were given standard synthetic pellet diet. One group served as control, and the other three were served as experimental groups. Decrease in the body weight of the high dose group was observed at 15 mg/kg/day, and no mortality occurred during the treatment period. High dose of imidacloprid caused a significant elevation of serum clinical chemistry parameters, serum glutamic oxaloacetic transaminase (SGOT), serum glutamic pyruvate kinase (SGPT), alkaline phosphatase (ALP) and total bilirubin (TBIL). Histology of liver and kidney indicates hepatotoxicity and nephrotoxicity at a high dose of imidacloprid. Based on the morphological, biochemical and histopathological analysis, it is evident that imidacloprid induced toxicological effects at 15 mg/kg/day to mice. The results of the present study demonstrate that IC had significant effects on body weight, liver functions and kidney (p < 0.05) at a dose of 15 mg/kg body weight. IC treatment 5 and 10 mg/kg/day may be considered as no observed adverse effect level (NOAEL) for mice. It was concluded that IC can cause hepatotoxicity and nephrotoxicity at a dose much lower than the LD50 (131 mg/kg body weight) in mice.

Hyperspectral sensing offers a quick and non-destructive alternative for assessing phenotypic parameters of plant physiological status and salt stress tolerance. This study compares the performance of published and modified spectral reflectance indices (SRIs) for estimating and predicting the growth and photosynthetic efficiency of two wheat cultivars exposed to three salinity levels (control, 6.0, and 12.0 dS m−1). Results show that individual SRIs based on visible- and near-infrared (VIS/VIS, NIR/VIS, and NIR/NIR) estimate and predict measured parameters considerably more efficiently than those based on shortwave-infrared (SWIR/VIS and SWIR/NIR), with the exception of some modified indices (the water balance index (WABI-1(1550, 482), WABI-2(1640, 482), and WABI-3(1650, 531)), normalized difference moisture index (NDMI(1660, 1742)), and dry matter content index (DMCI(1550, 2305)), which show moderate to strong relationships with measured parameters. Overall results indicate that modified SRIs can serve as rapid and non-destructive high-throughput alternative approaches for tracking growth and photosynthetic efficiency of wheat under salt stress field conditions.