Blue honeysuckle (Lonicera caerulea L.) is a deciduous shrub with perennial rootstock found in China. The objectives of this study were to explore the drought tolerance of blue honeysuckle, determine the effect of drought stress on two photosystems, and examine the mechanism of acquired drought tolerance. In this study, blue honeysuckle under four levels of simulated field capacity (100%, 85%, 75%, and 65% RH) was grown in split-root pots for drought stress treatment, for measuring the changes in chlorophyll content, photosynthetic characteristics, and leaf chlorophyll fluorescence parameters. The chlorophyll content of each increased under mild stress and decreased under moderate and severe stress. The net photosynthetic rate, transpiration rate, intercellular carbon dioxide concentration, and stomatal conductance of blue honeysuckle decreased with the increase in water stress. However, the water utilization rate and stomatal limit system increased under mild and moderate stress and decreased under severe stress. The maximum fluorescence (Fm), maximum photochemical efficiency, and quantum efficiency of photosystem II decreased with the decrease in soil water content, and the initial fluorescence increased significantly (p < 0.01). With the decrease in soil water content, the energy allocation ratio parameters decreased under severe drought stress. The main activity of the unit reaction center parameters first increased and then decreased. ABS/CSm, TRo/CSm, ETo/CSm, and REo/CSm gradually declined. After a comprehensive analysis, the highest scores were obtained under adequate irrigation (CK). Overall, we concluded that the water irrigation system of blue honeysuckle should be considered adequate.

Investigating differences in resistance to alkaline stress among three willow species can provide a theoretical basis for planting willow in saline soils. Therefore we tested three willow species (Salix matsudana, Salix gordejevii and Salix linearistipularis), already known for their high stress tolerance, to alkaline stress environment at different pH values under hydroponics. Root and leaf dry weight, root water content, leaf water content, chlorophyll content, photosynthesis and chlorophyll fluorescence of three willow cuttings were monitored six times over 15 days under alkaline stress. With the increase in alkaline stress, the water retention capacity of leaves of the three species of willow cuttings was as follows: S. matsudana > S. gordejevii > S. linearistipularis and the water retention capacity of the root system was as follows: S. gordejevii > S. linearistipularis > S. matsudana. The chlorophyll content was significantly reduced, damage symptoms were apparent. The net photosynthetic rate (Pn), rate of transpiration (E), and stomatal conductance (Gs) of the leaves showed a general trend of decreasing, and the intercellular CO2 concentration (Ci) of S. matsudana and S. gordejevii first declined and then tended to level off, while the intercellular CO2 concentration of S. linearistipularis first declined and then increased. The quantum yield and energy allocation ratio of the leaf photosystem II (PSII) reaction centre changed significantly (φPo, Ψo and φEo were obviously suppressed and φDo was promoted). The photosystem II (PSII) reaction centre quantum performance index and driving force showed a clear downwards trend. Based on the results it can be concluded that alkaline stress tolerance of three willow was as follows: S. matsudana > S. gordejevii > S. linearistipularis. However, since the experiment was done on young seedlings, further study at saplings stage is required to revalidate the results.

Salinity stress significantly impacts crops, disrupting their water balance and nutrient uptake, reducing growth, yield, and overall plant health. High salinity in soil can adversely affect plants by disrupting their water balance. Excessive salt levels can lead to dehydration, hinder nutrient absorption, and damage plant cells, ultimately impairing growth and reducing crop yields. Gallic acid (GA) and zinc ferrite (ZnFNP) can effectively overcome this problem. GA can promote root growth, boost photosynthesis, and help plants absorb nutrients efficiently. However, their combined application as an amendment against drought still needs scientific justification. Zinc ferrite nanoparticles possess many beneficial properties for soil remediation and medical applications. That\'s why the current study used a combination of GA and ZnFNP as amendments to wheat. There were 4 treatments, i.e., 0, 10 µM GA, 15 μM GA, and 20 µM GA, without and with 5 μM ZnFNP applied in 4 replications following a completely randomized design. Results exhibited that 20 µM GA + 5 μM ZnFNP caused significant improvement in wheat shoot length (28.62%), shoot fresh weight (16.52%), shoot dry weight (11.38%), root length (3.64%), root fresh weight (14.72%), and root dry weight (9.71%) in contrast to the control. Significant enrichment in wheat chlorophyll a (19.76%), chlorophyll b (25.16%), total chlorophyll (21.35%), photosynthetic rate (12.72%), transpiration rate (10.09%), and stomatal conductance (15.25%) over the control validate the potential of 20 µM GA + 5 μM ZnFNP. Furthermore, improvement in N, P, and K concentration in grain and shoot verified the effective functioning of 20 µM GA + 5 μM ZnFNP compared to control. In conclusion, 20 µM GA + 5 μM ZnFNP can potentially improve the growth, chlorophyll contents and gas exchange attributes of wheat cultivated in salinity stress. More investigations are suggested to declare 20 µM GA + 5 μM ZnFNP as the best amendment for alleviating salinity stress in different cereal crops.

Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the \"trilateral\" parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0-2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the \"trilateral\" parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0-2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.

The deleterious impact of osmotic stress, induced by water deficit in arid and semi-arid regions, poses a formidable challenge to cotton production. To protect cotton farming in dry areas, it\'s crucial to create strong plans to increase soil water and reduce stress on plants. The carboxymethyl cellulose (CMC), gibberellic acid (GA3) and biochar (BC) are individually found effective in mitigating osmotic stress. However, combine effect of CMC and GA3 with biochar on drought mitigation is still not studied in depth. The present study was carried out using a combination of GA3 and CMC with BC as amendments on cotton plants subjected to osmotic stress levels of 70 (70 OS) and 40 (40 OS). There were five treatment groups, namely: control (0% CMC-BC and 0% GA3-BC), 0.4%CMC-BC, 0.4%GA3-BC, 0.8%CMC-BC, and 0.8%GA3-BC. Each treatment was replicated five times with a completely randomized design (CRD). The results revealed that 0.8 GA3-BC led to increase in cotton shoot fresh weight (99.95%), shoot dry weight (95.70%), root fresh weight (73.13%), and root dry weight (95.74%) compared to the control group under osmotic stress. There was a significant enhancement in cotton chlorophyll a (23.77%), chlorophyll b (70.44%), and total chlorophyll (35.44%), the photosynthetic rate (90.77%), transpiration rate (174.44%), and internal CO2 concentration (57.99%) compared to the control group under the 40 OS stress. Thus 0.8GA3-BC can be potential amendment for reducing osmotic stress in cotton cultivation, enhancing agricultural resilience and productivity.

Nitrogen (N) is one of the most crucial elements for plant growth. However, a deficiency of N affects plant growth and development. Wedelia trilobata is a notorious invasive plant species that exhibits superior tolerance to adapt to environmental stresses. Yet, research on the growth and antioxidant defensive system of invasive Wedelia under low N stress, which could contribute to understanding invasion mechanisms, is still limited. Therefore, this study aims to investigate and compare the tolerance capability of invasive and native Wedelia under low and normal N conditions. Native and invasive Wedelia species were grown in normal and low-N conditions using a hydroponic nutrient solution for 8 weeks to assess the photosynthetic parameters, antioxidant activity, and localization of reactive oxygen species (ROS). The growth and biomass of W. trilobata were significantly (p < 0.05) higher than W. chinensis under low N. The leaves of W. trilobata resulted in a significant increase in chlorophyll a, chlorophyll b, and total chlorophyll content by 40.2, 56.2, and 46%, respectively, compared with W. chinensis. W. trilobata significantly enhanced antioxidant defense systems through catalase, peroxidase, and superoxide dismutase by 18.6%, 20%, and 36.3%, respectively, providing a positive response to oxidative stress caused by low N. The PCA analysis showed that W. trilobata was 95.3% correlated with physiological traits by Dim1 (79.1%) and Dim2 (16.3%). This study provides positive feedback on W. trilobata with respect to its comprehensive invasion mechanism to improve agricultural systems via eco-friendly approaches in N deficit conditions, thereby contributing to the reclamation of barren land.

BACKGROUND: The phenotypic traits of leaves are the direct reflection of the agronomic traits in the growth process of leafy vegetables, which plays a vital role in the selection of high-quality leafy vegetable varieties. The current image-based phenotypic traits extraction research mainly focuses on the morphological and structural traits of plants or leaves, and there are few studies on the phenotypes of physiological traits of leaves. The current research has developed a deep learning model aimed at predicting the total chlorophyll of greenhouse lettuce directly from the full spectrum of hyperspectral images.
RESULTS: A CNN-based one-dimensional deep learning model with spectral attention module was utilized for the estimate of the total chlorophyll of greenhouse lettuce from the full spectrum of hyperspectral images. Experimental results demonstrate that the deep neural network with spectral attention module outperformed the existing standard approaches, including partial least squares regression (PLSR) and random forest (RF), with an average R2 of 0.746 and an average RMSE of 2.018.
CONCLUSIONS: This study unveils the capability of leveraging deep attention networks and hyperspectral imaging for estimating lettuce chlorophyll levels. This approach offers a convenient, non-destructive, and effective estimation method for the automatic monitoring and production management of leafy vegetables.

The chlorophyll content reflects plants\' photosynthetic capacity, growth stage, and nitrogen status. Maize is one of the three widely planted gain crops in the world. In order to offer useful information for the development of chlorophyll content detectors of maize leaves, a single integrating sphere system was used to measure the transmittance and reflectance spectra of maize leaves over the wavelength range of 500-950 nm. The linear relationships of transmittance and reflectance with chlorophyll content were investigated. The feature wavelengths (FWs) sensitive to chlorophyll content were extracted from the full transmittance and reflectance spectra using the successive projections algorithm (SPA). The partial least squares regression (PLSR) models for predicting the chlorophyll content were established using the full spectra and extracted FWs. The results showed that there were obvious linear relationships between transmittance and reflectance with chlorophyll content of maize leaves and the best linear relationships were found at 709 nm and 714 nm, respectively, with the linear correlation coefficients of 0.801 and 0.696, and the root-mean-squares error (RMSEP) of 0.321 mg·g-1 and 0.405 mg·g-1, respectively. Eight and 6 FWs were extracted from the transmittance and reflectance spectra, respectively. The PLSR model established using the selected FWs from transmittance spectra had better prediction performance with RMSEP of 0.208 mg·g-1 than using full transmittance spectra. The built PLSR models using the full reflectance spectra and extracted FWs had poor robustness. This research offers some theoretical basis for developing a maize leaf chlorophyll content detector based on transmittance or reflectance.

Maize cultivated for dry grain covers approximately 197 million hectares globally, securing its position as the second most widely grown crop worldwide after wheat. Although spermidine and biochar individually showed positive impacts on maize production in existing literature, their combined effects on maize growth, physiology, nutrient uptake remain unclear and require further in-depth investigation. That\'s why a pot experiment was conducted on maize with spermidine and potassium enriched biochar (KBC) as treatments in Multan, Pakistan, during the year 2022. Four levels of spermidine (0, 0.15, 0.30, and 0.45mM) and two levels of potassium KBC (0 and 0.50%) were applied in completely randomized design (CRD). Results showed that 0.45 mM spermidine under 0.50% KBC caused significant enhancement in maize shoot length (11.30%), shoot fresh weight (25.78%), shoot dry weight (17.45%), root length (27.95%), root fresh weight (26.80%), and root dry weight (20.86%) over control. A significant increase in maize chlorophyll a (50.00%), chlorophyll b (40.40%), total chlorophyll (47.00%), photosynthetic rate (34.91%), transpiration rate (6.51%), and stomatal conductance (15.99%) compared to control under 0.50%KBC validate the potential of 0.45 mM spermidine. An increase in N, P, and K concentration in the root and shoot while decrease in electrolyte leakage and antioxidants also confirmed that the 0.45 mM spermidine performed more effectively with 0.50%KBC. In conclusion, 0.45 mM spermidine with 0.50%KBC is recommended for enhancing maize growth.

Light is a crucial environmental signal and a form of photosynthetic energy for plant growth, development, and nutrient formation. To explore the effects of light quality on the growth and nutritional qualities of greenhouse-grown lettuce (Lactuca sativa L.), lettuce was cultivated under supplementary white (W) light-emitting diodes (LEDs); white plus ultraviolet A LEDs (W+UV); white plus far-red LEDs (W+FR); and the combination of white, far-red, and UV-A LEDs (W+FR+UV) for 25 days, with lettuce grown under natural sunlight used as the control. The results indicate that the leaf length and leaf width values for lettuce grown under the W+FR+UV treatment were significantly higher than those of lettuce grown under other supplementary light treatments. The highest values of shoot fresh weight, shoot dry weight, root fresh weight, and root dry weight were recorded under the W+FR treatment (4.0, 6.0, 8.0, and 12.4 times higher than those under the control treatment, respectively). Lettuce grown under the W+FR treatment exhibited the highest total chlorophyll content (39.1%, 24.6%, and 16.2% higher than that under the W, W+UV, and W+FR+UV treatments, respectively). The carotenoid content of lettuce grown under the W+FR treatment was the highest among all treatments. However, the root activity of greenhouse-grown lettuce was the highest under the W+FR+UV treatment. Soluble sugar content, cellulose content, and starch content in the lettuce responded differently to the light treatments and were highest under the W+UV treatment. In summary, supplementary light promoted growth and nutrient accumulation in lettuce. Specifically, white plus far-red light promoted lettuce growth, and white plus UV increased some specific compounds in greenhouse-grown lettuce. Our findings provide valuable references for the application of light-supplementation strategies to greenhouse lettuce production.