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Assessing and predicting quality of groundwater is crucial in managing groundwater availability effectively. In the current study, groundwater quality was thoroughly appraised using various indexing methods, including the drinking water quality index (DWQI), pollution index of heavy metals (HPI), pollution index (PI), metal index (MI), degree of contamination (Cd), and risk indicators, like hazard quotient (HQ) and total hazard indicator (HI). The assessments were augmented through multivariate analytical techniques, models based on recurrent neural networks (RNNs), and integration of geographic information system (GIS) technology. The analysis measured physicochemical parameters across 48 groundwater wells from El-Menoufia region, revealing distinct water types influenced by ion exchange, rock-water interactions, and silicate weathering. Notably, the groundwater showed elevated levels of certain metals, particularly manganese (Mn) and lead (Pb), exceeding the drinking water limits. The DWQI deemed the bulk of the tested samples suitable for consumption, assigning them to the ''good'' category, whereas a small number were considered inferior quality. The HPI, MI, and Cd indices indicated significant pollution in the central study region. The PI revealed that Pb, Mn, and Fe were significant contributors to water pollution, falling between classes IV (strongly affected) and V (seriously affected). HQ and HI analyses identified the central area of the study as particularly prone to metal contamination, signifying a high risk to children via oral and dermal routes and to adults through oral exposure alone (non-carcinogenic risk). The adults had no health risks due to dermal contact. Finally, the RNN simulation model effectively predicted the health and water quality indices in training and testing series. For instance, the RNN model excelled in predicting the DWQI, with three key parameters being crucial. The model demonstrated an excellent fit on the training set, achieving an R2 of 1.00 with a very low root mean of squared error (RMSE) of 0.01. However, on the testing set, the model's performance slightly decreased, showing an R2 of 0.96 and an RMSE of 2.73. Regarding HPI, the RNN model performed exceptionally well as the primary predictor, with R2 values of 1.00 (RMSE = 0.01) and 0.93 (RMSE = 27.35) for the training and testing sets, respectively. This study provides a unique perspective for improving the integration of various techniques to gain a more comprehensive understanding of groundwater quality and its associated health risks, with a strong focus on feature selection strategies to enhance model accuracy and interpretability.

Abstract 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.

IntroductionPrecise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer segmentation model for precise semantic segmentation of strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions.MethodsThree distinct Mix Transformer encoders - MiT-B0, MiT-B3, and MiT-B5 - were thoroughly analyzed to enhance disease detection, targeting diseases such as Angular leaf spot, Anthracnose rot, Blossom blight, Gray mold, Leaf spot, Powdery mildew on fruit, and Powdery mildew on leaves. The dataset consisted of 2,450 raw images, expanded to 4,574 augmented images. The Segment Anything Model integrated into the Roboflow annotation tool facilitated efficient annotation and dataset preparation.ResultsThe results reveal that MiT-B0 demonstrates balanced but slightly overfitting behavior, MiT-B3 adapts rapidly with consistent training and validation performance, and MiT-B5 offers efficient learning with occasional fluctuations, providing robust performance. MiT-B3 and MiT-B5 consistently outperformed MiT-B0 across disease types, with MiT-B5 achieving the most precise segmentation in general.DiscussionThe findings provide key insights for researchers to select the most suitable encoder for disease detection applications, propelling the field forward for further investigation. The success in strawberry disease analysis suggests potential for extending this approach to other crops and diseases, paving the way for future research and interdisciplinary collaboration.

Abstract 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.

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.

Abstract Evaluating grouLindwater quality and associated hydrochemical properties is critical to manage groundwater resources in arid and semiarid environments. The current study examined groundwater quality and appropriateness for agriculture in the alluvial aquifer of Makkah Al-Mukarramah Province, Saudi Arabia, utilizing several irrigation water quality indices (IWQIs) such as irrigation water quality index (IWQI), total dissolved solids (TDS), sodium adsorption ratio (SAR), potential salinity (PS), magnesium hazard (MH), and residual sodium carbonate (RSC) assisted by multivariate modeling and GIS tools. One hundred fourteen groundwater wells were evaluated utilizing several physicochemical parameters, which indicating that the primary cation and anion concentrations were as follows: Na+ > Ca2+ > Mg2+ > K+, and Cl− > SO4 2˗ > HCO3 ˗ > NO3 ˗ > CO3 2˗, respectively, reflecting Ca–HCO3, Na–Cl, and mixed Ca–Mg–Cl–SO4 water facies under the stress of evaporation, saltwater intrusion, and reverse ion exchange processes. The IWQI, TDS, SAR, PS, MH, and RSC across two studied regions had mean values of 64.86, 2028.53, 4.98, 26.18, 38.70, and − 14.77, respectively. For example, the computed IWQI model indicated that approximately 31% of samples fell into the no restriction range, implying that salinity tolerance crops should be avoided, while approximately 33% of samples fell into the low to moderate restriction range, and approximately 36% of samples fell into the high to severe restriction range for irrigation, implying that moderate to high salt sensitivity crops should be irrigated in loose soil with no compacted layers. The partial least squares regression model (PLSR) produced a more accurate assessment of six IWQIs based on values of R 2 and slope. In Val. datasets, the PLSR model generated strong estimates for six IWQIs with R 2 varied from 0.72 to 1.00. There was a good slope value of the linear relationship between measured and predicted for each parameter and the highest slope value (1.00) was shown with RSC. In the PLSR models of six IWQIs, there were no overfitting or underfitting between the measuring, calibrating, and validating datasets. In conclusion, the combination of physicochemical characteristics, WQIs, PLSR, and GIS tools to assess groundwater suitability for irrigation and their regulating variables is beneficial and provides a clear picture of water quality.

The current study aimed to design and test the accuracy of a variable-rate control metering mechanism (VRCMM) and a remote smart monitoring system (RSMS) for a precision sugarcane transplanter based on IoT technology. The VRCMM is used to operate the seedling metering device at different speeds using a stepper motor based on the travel speed, and the RSMS was employed to evaluate of the three basic parameters of seedling amount, optimum rate, and missed rate. Two types of sensors were used for detecting the sugarcane seedling (SS) and travel speed, including one ultrasonic sensor and one infrared RPM sensor. The study was performed at five travel speeds and four transplant spacings. The findings of laboratory tests showed that the mean record of the relative error between the desired stepper motor speed of the VRCMM and the real value was 3.39%, and it increased with increasing the travel speed. as Additionally, the speed regulation performance was in agreement with the transplanting index. The change in RSMS accuracy is obvious when the travel speed is high and the transplant spacing is small. The RSMS accuracy drops sharply, revealing a leaping change. In conclusion, the smart and intelligent designed sugarcane transplanter would be very useful in sugarcane production.

Accurately estimating crop performance under various fertilizer levels in a rapid and non-destructive manner has become a vital aspect of precision agriculture technology for both economic and environmental benefits. This study aimed to estimate different sugar beet parameters, such as total chlorophyll (Chlt), chlorophyll a (Chla), chlorophyll b (Chlb), root yield (RY), sugar yield (SY), and sugar content (SC) under five nitrogen (N) levels (0, 30, 60, 90, and 120 kg N ha−1). This was achieved by using a combination of the gradient boosting regression (GBR) model with published and newly developed two- and three-band spectral indices (2D- and 3D-SRIs). The results showed that the N levels had the highest proportion of variations (80.4–92.9%) for all parameters, except for SC, which had more variation (59.9%) according to year than the N levels (37.2%). All parameters, except SC, showed a significant increase with gradually increasing N levels. Additionally, the N levels displayed linear and strong positive relationships with the chlorophyll parameters, and linear and strong negative relationships with SC, while these relationships were quadratic and strong with RY and SY. Several published and novel 3D-SRIs exhibited moderate to strong relationships (R2 = 0.65–0.89) with all parameters. The newly developed 3D-SRIs, which involve wavelengths from the visible, near-infrared, and red-edge regions, such as NDI536, 538, 534, NDI738, 750, 542, and NDI448, 734, 398, were effective in accurately estimating all parameters. Combining 2D-SRIs, 3D-SRIs, and the aggregate of all spectral indices (ASRIs) with GBR models could be a robust strategy for estimating the six observed parameters with reasonable precision. The GBR-ASF-6 SRIs and the GBR-ASF-7 SRIs models performed better in predicting Chl content and SC with R2 values of 0.99 and 0.99 (RMSE = 0.073 and 1.568) for the training dataset and R2 values of 0.65 and 0.78 (RMSE = 0.354 and 6.294) for the testing datasets, respectively. The obtained results concluded that published and newly developed 3D-SRIs, GBR based on 2D-SRIs or 3D-SRIs, and the aggregate of all ASRIs can be used in practice to accurately estimate the Chl content, production, and quality of sugar beet across a wide range of N levels under semiarid conditions.

The use of automatic systems in the agriculture sector enhances product quality and the country’s economy. The method used to sort fruits and vegetables has a remarkable impact on the export market and quality assessment. Although manual sorting and grading can be performed easily, it is inconsistent, time-consuming, expensive, and highly influenced by the surrounding environment. In this regard, this study aimed to design and optimize the performance of a low-cost, multi-purpose, automatic RGB color-based sensor for sorting fruits. The proposed automatic color sorting system consists of hardware components including a machine frame, belt and pulleys, conveyor belt, scanning zone, plastic boxes, electric components (stepper motors, RGB color sensors, Arduino Mega, motor drivers), and software components (Arduino IDE version 2.2.1 and C++). Calibration was performed for the light intensity sensor to measure the light intensity inside the scanning zone, the conveyor speed sensor, and the RGB color sensors by testing the RGB color channels. The sensor, the height, conveyor belt color, and light intensity should be carefully adjusted to ensure a high performance of the color-based sorting system. The results showed that the appropriate sensor height ranged from 15 to 30 mm, the optimum color of the conveyor belt was black, and scanning the objects at a light intensity of 25 lux achieved the best output signals. The RGB color sensors achieved an analytical performance similar to that obtained with manual sorting without requiring the use of computers for image processing like other automatic sorting systems do in order to gather RGB data.

Moisture and potassium deficiency are two of the main limiting variables for squash crop performance in many water-stressed places worldwide. If major output decreases are to be avoided, it is critical to detect signs of crop stress as early as possible in the growth cycle. Proximal remote sensing can be a reliable technique for offering a rapid and precise instrument and localized management tool. This study tested the ability of proximal hyperspectral remotely sensed data to predict squash traits in two successive seasons (spring and fall) with varying moisture and potassium rates. Spectral data were collected from drip-irrigated squash that had been treated to varied rates of irrigation and potassium fertilization over both investigated seasons. To forecast potassium-use efficiency (KUE), chlorophyll meter (Chlm), water-use efficiency (WUE), and seed yield (SY) of squash, different commonly used and newly-introduced spectral index values for three bands (3D-SRIs), as well as a Decision Tree (DT) model, were evaluated. The results revealed that the newly constructed three-band SRIs based on the wavelengths of the visible (VIS), near-infrared (NIR), and red-edge regions were sensitive enough to measure the four tested parameters of squash in this study. For instance, NDI558,646,708 presented the highest R2 of 0.75 for KUE, NDI744,746,738 presented the highest R2 of 0.65 for Chlm, and NDI670,628,392 presented the highest R2 of 0.64 for SY of squash. The results further demonstrated that the principal component analysis (PCA) demonstrated the ability to distinguish moisture stress from potassium deficiency stress at the flowering stage onwards. Combining 3D-SRIs, DT-based bands (DT-b), and the aggregate of all spectral characteristics (ASF) with DT models would be an effective strategy for estimating four observed parameters with appropriate accuracy. For example, the model’s approximately 30 spectral characteristics were extremely important for predicting KUE. Its outputs with R2 were, for the training and validation datasets, 0.967 (RMSE = 0.175) and 0.818 (RMSE = 0.284), respectively. For measuring Chlm, the DT-DT-b-20 model demonstrated the best. In the training and validation datasets, the R2 value was 0.993 (RMSE = 0.522) and 0.692 (RMSE = 2.321), respectively. The overall outcomes showed that proximal-reflectance-sensing-based 3D-SRIs and DT models based on 3D-SRIs, DT-b, and ASF could be used to evaluate the four tested parameters of squash under different levels of irrigation regimes and potassium fertilizer.

Global warming is by far the most significant issue caused by climate change. Over the past few decades, heat stress has intensified into a serious issue that has a negative impact on crop production. Hence, it is crucial to modify cultivation systems to cope with this kind of stress, particularly in arid dry regions. In comparison to open-field cultivation, tomato production under protected cultivation techniques in walk-in tunnels that are suited for different farmers’ financial abilities was evaluated during the late summer season. The studied tunnels included a shaded net tunnel with natural ventilation, net tunnel with a fogging system and plastic tunnel with evaporative cooling (wet pad and fans). For the operation of fogging and evaporative cooling systems, solar energy was used as a sustainable, eco-friendly energy source. The results indicated that the solar energy system successfully operated the studied cooling systems. All studied protective cultivation techniques mitigated heat stress on tomato plant and improved the microclimate under walk-in tunnels. Moreover, evaporative cooling and fogging systems significantly increased plant leaf area, cell membrane efficiency and the contents of chlorophyll, relative water and proline compared to the net tunnel with natural ventilation. Furthermore, a marked reduction in physiological disorders was noticed. Improved physiological and biochemical parameters and limited physiological diseases led to higher fruit set, marketable fruit yield and total productivity. The percentage of marketable fruit yield increased by around 31.5% with an evaporative cooling system, 28.8% with a fogging system and 17% with a shaded net tunnel with no positive cooling as compared to an open field. However, the plants grown in open-field cultivation without protection significantly deteriorated from heat stress and had a high incidence of physiological disorders. The most incident physiological disorders were blossom-end rot, cracking, internal white tissues, sunscald, puffiness, blotchy ripening, cat face and exserted stigma. It is recommended to use a solar energy system to modify microclimate conditions through fogging or evaporative cooling under walk-in tunnels to ameliorate heat stress on grown tomato in the late summer season for higher fruit yield and fewer physiological disorders.

Ozone is an important air pollutant that causes many challenges for human health, such as lung diseases. The negative impacts of exogenous ozone on cultivated plants have been discussed in several publications, but the positive impacts are less investigated. The current study is an attempt to answer the following question: is there any positive contribution of ozone pretreatment in growing plants under stress? Plants grown in unheated plastic greenhouses suffer from cold stress during the winter when the temperature drops to 5–10 °C. This stress can also be enhanced under saline soil conditions in arid regions. Treatments involving different ozone application methods (seed priming and seedling foliar application) and cold pretreatment (4 °C for 36 h) were compared with untreated controls of two sweet pepper hybrids (Zidenka and Lirica) in an unheated plastic greenhouse. In general, the Lirica hybrid outperformed the Zidenka hybrid in growth and fruit yield and showed good adaptation to cold stress resulting from ozone treatment through foliar application on seedlings. Moreover, cold treatment was associated with higher values of the studied parameters compared to the control (untreated). A concentration of 20 ppm of ozone in the first season and as 30 ppm of ozone in the second season increased plant leaf area, proline content, and APX activity, all of which improved the plant’s defense mechanisms against cold stress. These benefits contributed to high fruit sets; early, total, and marketable fruit yields; and fruit quality under cold stress. The highest yield (16.52 kg m−2) was attained with 30 ppm ozone applied as a seedling spray, compared with 10.07 kg m−2 in the control. Therefore, the foliar application of ozone up to 30 ppm can be recommended for sweet pepper hybrids of Lirica under unheated plastic greenhouse conditions. Further investigations are needed to study the interaction of combined ozone and cold stress, as well as various levels of soil salinity.

Point samples and laboratory testing have historically been used to evaluate fruit quality criteria. Although this method is precise, it is slow, expensive, and destructive, making it unsuitable for large-scale monitoring of these parameters. The main objective of this research was to develop a non-invasive protocol by combining color RGB indices (CIs) and previously published and newly developed three-band spectral reflectance indices (SRIs) with a decision tree (DT) model to evaluate the fruit quality parameters of navel orange. These parameters were brightness (L*), red–green (a*), blue–yellow (b*), chlorophyll meter (Chlm), total soluble solids (TSS), and TSS/acid ratio. The characteristics of fruit quality of navel orange samples were measured at various stages of ripening. The outcomes demonstrated that at various levels of ripening, the fruit quality parameters, RGB imaging indices, and published and newly developed three-band SRIs differed. The newly developed three-band SRIs based on the wavelengths of blue, green, red, red-edge, and NIR are most effective for estimating the six measured parameters in this study. For example, NDI574,592,724, NDI572,584,724, and NDI574,722,590 had the largest R2 value (0.90) with L*, whereas NDI526,664,700 and NDI524,700,664 exhibited the highest R2 value (0.97) with a*. Moreover, integrating CIs and SRIs with the DT model has provided a potentially useful tool for the accurate measurement of the six studied parameters. For instance, the DT-SRIs-CIs-30 model performed better in terms of measuring a* using 30 various indices. The R2 value was 0.98 and RMSE = 1.121 in the cross-validation, while R2 value was 0.964 and RMSE = 2.604 in the test set. Otherwise, based on the fusion of five various indices, the DT-SRIs-CIs-5 model was the most precise for recognizing b* (R2 = 0.957 and 0.929, with RMSE = 1.713 and 3.309 for cross-validation and test set, respectively). Overall, this work proves that integrating the different characteristics of proximal reflectance sensing systems such as color RGB indices and SRIs via the DT model may be considered a reliable instrument for evaluating the quality of different fruits.

Water scarcity is a major constraint for food production, particularly in arid and semi-arid environments. In this regard, selecting the best irrigation technique is crucial to overcome water scarcity and enhance water productivity (WP) with no significant yield loss. This study aimed to assess the impact of irrigation techniques of every furrow irrigation (EFI), alternate furrow irrigation (AFI), and drip irrigation (DI), as well as the flow rate, on irrigation system performance parameters, yield, water productivity of cauliflower crop and soil salinity during the two successive growing seasons of 2017/2018 and 2018/2019 under field conditions. The treatments comprised three different irrigation inflow rates: Q1 = 0.47 L/s, Q2 = 0.95 L/s, and Q3 = 1.43 L/s. For both investigated seasons, the AFI + Q3 treatment produced the best water distribution uniformity (DU) and water application efficiency (AE) of 85.10% and 72.73%, respectively, of the surface irrigation, and DI methods across the two growing seasons produced the highest DU of 95%. DI produced the highest cauliflower curd yield (18.12 Mg/fed), followed by EFI + Q3 (12.285 Mg/fed) and AFI + Q3 (11.905 Mg/fed). The maximum mean WP value of 10.6 kg/m3 was recorded with DI, followed by AFI + Q3 (6.24 kg/m3), across the two growing seasons. DI, AFI + Q3, AFI + Q2, AFI + Q1, EFI + Q3, and EFI + Q2 saved irrigation water by 32.63, 28.71, 21.22, 18.04, 10.48, and 3.18%, respectively, compared with EFI + Q1 across the two growing seasons. During both seasons, the average value using the drip irrigation system was 3.60 dS/m. Considering the annual leaching requirements of soil, climate change conditions, and fixed costs, we recommend the use of a drip irrigation system in clayey soil to produce cauliflower, followed by the use of the alternative furrow irrigation method to enable the aeration of the same soil for a lower cost.

Coastal environmental assessment techniques have evolved into one of the most important fields for the long-term development and management of coastal zones. So, the overall aim of the present investigation was to provide effective approaches for making informed decisions about the Gamasa coast sediment quality. Over a two-year investigation, sediment samples were meticulously collected from the Gamasa estuary and littoral shelf. The inductively coupled plasma mass spectra (ICP-MS) was used to the total concentrations of Al, Fe, Ti, Mg, Mn, Cu, P, V, Ba, Cr, Sr, Co, Ni, Zn, Pb, Zr, and Ce. Single elements environmental pollution indices including the geoaccumulation index (Igeo), contamination factor (CF), and enrichment factor (EF), as well as multi-elements pollution indices comprising the potential ecological risk index (RI), degree of contamination (Dc), and pollution load index (PLI) were used to assess the sediment and the various geo-environmental variables affecting the Mediterranean coastal system. Furthermore, the Dc, PLI, and RI were estimated using the random forest (RF) and Back-Propagation Neural Network (BPNN) depending on the selected elements. According to the Dc results, all the investigated sediment samples categories were considerably contaminated. Cr, Co, Ni, Cu, Zr, V, Zn, P, and Mn showed remarkable enrichment in sediment samples and were originated from anthropogenic sources based on the CF, EF, and Igeo data. Moreover, the RI findings revealed that all the samples tested pose a low ecologically risk. Meanwhile, based on PLI, 70% of the Gamasa estuary samples were polluted, while 93.75% of littoral shelf sediment was unpolluted. The BPNNs -PCs-CD-17 model performed the best and demonstrated a better association between exceptional qualities and CD. With R2 values of 1.00 for calibration (Cal.) and 1.00 for validation (Val.). The BPNNs -PCs-PLI-17 models performed the best in terms of measuring PLI with respective R2 values of 1.00 and 0.98 for the Cal. and Val. datasets. The findings showed that the RF and BPNN models may be used to precisely quantify the pollution indices (Dc, PLI, and RI) in calibration (Cal.) and validation (Val.) datasets utilizing potentially toxic elements of surface sediment.

The primary issues in collecting biochemical information in a large area using chemical laboratory procedures are low throughput, hard work, time-consuming, and requiring several samples. Thus, real-time and precise estimation of biochemical variables of various fruits using a proximal remote sensing based on spectral reflectance is critical for harvest time, artificial ripening, and food processing, which might be beneficial economically and ecologically. The main goal of this study was to assess the biochemical parameters of banana fruits such as chlorophyll a (Chl a), chlorophyll b (Chl b), respiration rate, total soluble solids (TSS), and firmness using published and newly developed spectral reflectance indices (SRIs), integrated with machine learning modeling (Artificial Neural Networks; ANN and support vector machine regression; SVMR) at different ripening degrees. The results demonstrated that there were evident and significant differences in values of SRIs at different ripening degrees, which may be attributed to the large variations in values of biochemical parameters. The newly developed two-band SRIs are more effective at measuring different biochemical parameters. The SRIs that were extracted from the visible (VIS), near-infrared (NIR), and their combination showed better R2 with biochemical parameters. SRIs combined with ANN and SVMR would be an effective method for estimating five biochemical parameters in the calibration (Cal.) and validation (Val.) datasets with acceptable accuracy. The ANN-TSS-SRI-13 model was built to determine TSS with greater performance expectations (R2 = 1.00 and 0.97 for Cal. and Val., respectively). Furthermore, the model ANN-Firmness-SRI-15 was developed for determining firmness, and it performed better (R2 = 1.00 and 0.98 for Cal. and Val., respectively). In conclusion, this study revealed that SRIs and a combination approach of ANN and SVMR models would be a useful and excellent tool for estimating the biochemical characteristics of banana fruits.

Rapid urbanization and industrialization around the world have created massive amounts of organic residues, which have been prioritized for conversion into valuable resources through the composting process to keep their harmful effect at a minimum. This research aimed to assess the influence of active and passive aeration on composting mass of sugar beet residues in the case of using additives (e.g., charcoal only or manure only or combination). Some physicochemical properties of composting mass were analyzed on certain days of composting. Some parameters including temperature–time profile, carbon to nitrogen ratio (C/N ratio), moisture content, electrical conductivity, pH, germination and microbial population enumeration of compost were measured. Cress germination test was conducted for each medium of germination which contains a mixture of soil and compost (at a ratio of 3:1) taken from each treatment. The results showed that temperature–time profile data of composting mass showed an irregularity. Forcedly aerated composting mass did not demonstrate a thermophilic phase while passively aerated ones did not show a mesophilic phase. Carbon to nitrogen (C/N) ratio reduction was greater in most forcedly aerated composting mass than passively aerated on days from 1 to 33 of composting period. The results further showed that electrical conductivity decreased at the end of the composting period where it ranged from 2.55 to 3.1 dS/m. Germination medium containing forcedly aerated compost treated with a combination of charcoal and manure achieved the highest germination index which was higher than the control sample by 58.63% followed by forcedly aerated composting mass treated by charcoal only which exceeded the control sample by 5.35%. Strong correlation coefficient (r > 0.80) for the relationship between germination index and number of bacteria was obtained on day 17th of composting period.

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.

Monitoring strategic agricultural crops in terms of crop growth performance, by accurate cost-effective and quick tools is crucially important in site-specific management to avoid crop reductions. The availability of commercial high resolution satellite images with high resolution (spatial and spectral) as well as in situ spectra measurements can help decision takers to have deep insight on crop stress in a certain region. The research attempts to examine remote sensing dataset for forecasting wheat crop (Sakha 61) characteristics including the leaf area index (LAI), plant height (plant-h), above ground biomass (AGB) and Soil Plant Analysis Development (SPAD) value of wheat across non-stress, drought and salinity-induced stress in the Nile Delta region. In this context, the ability of in situ spectroradiometry measurements and QuickBird high resolution images was evaluated in our research. The efficiency of Random Forest (RF) and Artificial Neural Network (ANN), mathematical models was assessed to estimate the four measured wheat characteristics based on vegetation spectral reflectance indices (V-SRIs) extracted from both approaches and their interactions. Field surveys were carried out to collect in situ spectroradiometry measurements concomitant with the acquisition of QuickBird imagery. The results demonstrated that several V-SRIs extracted from in situ spectroradiometry data and the QuickBird image correlated with the LAI, plant-h, AGB, and SPAD value of wheat crop across the study site. The determination coefficient (R2) values of the association between V-SRIs of in situ spectroradiometry data and various determined wheat characteristics varied from 0.26 to 0.85. The ANN-GSIs-3 was found to be the optimum predictive model, demonstrating a greater relationship between the advanced features and LAI. The three features of V-SRIs comprised in this model were strongly significant for the prediction of LAI. The attained results indicated high R2 values of 0.94 and 0.86 for the training and validation phases. The ANN-GSIs-3 model constructed for the determination of chlorophyll in the plant which had higher performance expectations (R2 = 0.96 and 0.92 for training and validation datasets, respectively). In conclusion, the results of our study revealed that high resolution remote sensing images such as QuickBird or similar imagery, and in situ spectroradiometry measurements have the feasibility of providing necessary crop monitoring data across non-stressed and stressed (drought and salinity) conditions when integrating V-SRIs with ANN and RF algorithms.

Egypt has limited agricultural land, associated with the scarcity of irrigation water and rapid population growth. Hydroponic farming, seawater desalination and magnetic treatment are among the practical solutions for sustaining rapid population growth. In this regard, the main objective of the present research study was to design and construct a hierarchical engineering unit as a hydroponic farming system (soilless) to produce an iceberg lettuce crop using magnetically treated saline water. The treatments included four types of irrigation water: common irrigation water (IW1) with an electrical conductivity (EC) of 0.96 dS/m as a control treatment, magnetically treated common irrigation water (IW2) with an EC of 0.96 dS/m, saline water (IW3) with an EC of 4.56 dS/m and magnetically treated saline water (IW4) with an EC of 4.56 dS/m; three depletion ratios (DR) of field capacity (DR0 = 50%, DR1 = 60% and DR2 = 70%) and three slopes of hydroponic pipes (S1 = 0.0%, S2 = 0.025% and S3 = 0.075%). The results revealed that seawater contributed 7.15% to produce iceberg lettuce in the hydroponic system. The geometric parameter, the slope of the pipes, influenced the obtained luminous intensity by an average increase of 21% and 71% for S2 and S3, respectively, compared with the zero slope (horizontal pipes). Magnetization of irrigation water increased the total soluble solids (TSS) and enhanced the fresh weight and water productivity of both iceberg lettuce varieties used. The maximum percentages of TSS were 5.20% and 5.10% for lemur and iceberg 077, respectively, for the combination IW4DR2S2. The highest values of fresh weight and water productivity of 3.10 kg/m and 39.15 kg/m3 were recorded with the combinations IW3DR2S3 and IW4DR1S3, respectively, for lemur and iceberg lettuce. The percentages of these increases were 109.46% and 97.78%, respectively, when compared with the combination IW1DR0S1. The highest values of iceberg lettuce 077 fresh weight and water productivity were 2.93 kg/m and 36.15 kg/m3, respectively, which were recorded with the combination IW4DR1S3. The percentages of these increases were 112.32% and 120.56%, respectively, when compared with IW1DR0S1 (the control treatment).

This research intends to offer a scientific foundation for environmental monitoring and early warning which will aid in the environmental protection management of Qaroun Lake. Qaroun Lake is increasingly influenced by untreated wastewater discharge from many anthropogenic activities, making it vulnerable to pollution. For that, six environmental pollution indices, namely contamination factor (Cf), enrichment factor (EF), geo-accumulation index (Igeo), degree of contamination (Dc), pollution load index (PLI), and potential ecological risk index (RI), were utilized to assess the bottom sediment and to determine the different geo-environmental variables affecting the lake system. Cluster analysis (CA), and principal component analysis (PCA) were used to explore the potential pollution sources of heavy metal. Moreover, the efficiency of partial least-square regression (PLSR) and multiple linear regression (MLR) were tested to assess the Dc, PLI, and RI depending on the selected elements. The sediment samples were carefully collected from 16 locations of Qaroun Lake in two investigated years in 2018 and 2019. Total concentrations of Al, As, Ba, Cd, Co, Cr, Cu, Fe, Ga, Hf, Li, Mg, Mn, Mo, Ni, P, Pb, Sb, Se, Zn, and Zr were quantified using inductively coupled plasma mass spectra (ICP-MS). According to the Cf, EF, and Igeo results, As, Cd, Ga, Hf, P, Sb, Se, and Zr demonstrated significant enrichment in sediment and were derived from anthropogenic sources. According to Dc results, all collected samples were categorized under a very high degree of contamination. Further, the results of RI showed that the lake is at very high ecological risk. Meanwhile, the PLI data indicated 59% of lake was polluted and 41% had PLI < 1. The PLSR and MLR models based on studied elements presented the highest efficiency as alternative approaches to assess the Dc, PLI, and RI of sediments. For examples, the validation (Val.) models presented the best performance of these indices, with R2val = 0.948–0.989 and with model accuracy ACCv = 0.984–0.999 for PLSR, and with R2val = 0.760–0.979 and with ACCv = 0.867–0.984 for MLR. Both models for Dc, PLI, and RI showed that there was no clear overfitting or underfitting between measuring, calibrating, and validating datasets. Finally, the combinations of Cf, EF, Igeo, PLI, Dc, RI, CA, PCA, PLSR, and MLR approaches represent valuable and applicable methods for assessing the risk of potentially harmful elemental contamination in the sediment of Qaroun Lake.

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.

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.

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

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.

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.

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.

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.

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.

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.