Genomics and Physiology of Chlorophyll Fluorescence Parameters in Hordeum vulgare L. under Drought and Salt Stresses

ZnONPs alleviate cadmium toxicity in pepper by reducing oxidative damage

Cadmium (Cd) is a genotoxic heavy metal causing severe toxicity symptoms in plants, which has been a major threat to worldwide crop production. Recently, nanoparticles (NPs) have been employed as a novel strategy to facilitate the Cd stress and act as nano-fertilizers directly. Therefore, this study aims to explore the effects of zinc oxide nanoparticles (ZnONPs; 15 mg/L) on plant growth, photosynthetic activity, antioxidant activity and root morphology in Capsicum chinense Jacq. under Cd (CdCl2; 50 μM/L) stress. The pepper plants were treated with Cd stress for 14 days, and the treatment was given directly into the hydroponic solution, while ZnONPs were applied as foliar spray two times a day (9 a.m. - 3 p.m.). The results revealed that Cd stress inhibited plant growth and biomass by impairing photosynthesis in photosystem function, gas exchange parameters, root activity, and morphology. In contrast, ZnONPs application notably reinforced the plant growth traits, increased photosynthesis efficiency in terms of chlorophyll content, SPAD index, gas exchange parameters and PSII maximum efficiency (Fv/Fm) and decreased Cd accumulation in leaf and root by 30% and 75%. Furthermore, ZnONPs efficiently restricted the hydrogen peroxide, superoxide ion (H2O2, O2•-). They restored cellular integrity (less MDA production) by triggering the antioxidant enzyme activities such as superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), ascorbate peroxidase (APX) and glutathione reductase (GR), protein content, sugar level and proline content. Besides, ZnONPs treatment enhanced secondary metabolites (phenols and flavonoids) contents and these metabolites potentially restricted excess H2O2 accumulation. In conclusion, our findings deciphered the potential functions of ZnONPs in alleviating Cd-induced phytotoxicity in pepper plants by boosting biomass production, photosynthesis, secondary metabolism and reducing oxidative stress.

Effects of exogenous 2,4-epibrassinolide on photosynthetic traits of 53 cowpea varieties under NaCl stress

This study examined the effects of exogenous 2,4-epibrassinolide (EBR) on photosynthetic traits of 53 cowpea varieties under NaCl stress. The results of different analysis and correlation analysis showed that these 53 germplasm resources had rich genetic diversity, and significant correlations existed among various photosynthetic traits. Under NaCl stress, Pn was highly significantly positively correlated with Gs and Tr and extremely significantly negatively correlated with Ci. Under EBR treatment, Pn was extremely significantly positively correlated with Gs, Ci, Tr and it was significantly negatively correlated with Chla, Chlb, Chl(a + b), and Y(II). Under EBR treatment and NaCl stress, Pn was extremely significantly positively correlated with Tr, and significantly positively correlated with Gs and carotenoid reflectance index. Principal component analysis shows that in CK group and EBR treatment group, cowpea photosynthesis traits can be summarized as six principal components, contributing 82.298 and 83.046%, respectively, can replace 19 photosynthetic traits to evaluate 53 cowpea varieties; under NaCl stress group and EBR + NaCl stress group, photosynthesis traits can be summarized as seven principal components, with cumulative contribution rate of 84.564 and 85.742%, respectively. In the untreated case, the cluster analysis was used to screen 32 cowpea varieties exhibiting the strongest photosynthetic capacity. Under salt stress, six of these varieties were classified as salt-tolerant. Under EBR spraying + salt stress, all four varieties showed strong photosynthetic capacity, and EBR showed the best relief of salt stress. The results of this study will provide a theoretical basis for the application of exogenous EBR to alleviate cowpea salt stress damage.

High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images

Chlorophyll content reflects plants' photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared. The most sensitive spectral (SVIs) and color vegetation indices (CVIs) for chlorophyll content were extracted and evaluated as reliable estimators of chlorophyll content. Using an ASD FieldSpec 4 Hi-Res spectroradiometer and a portable red, green, and blue (RGB) camera, 3600 hyperspectral reflectance measurements and 800 RGB images were acquired from lettuce grown across a gradient of nutrient levels. Ground measurements of leaf chlorophyll were acquired using an SPAD-502 m calibrated via laboratory chemical analyses. The results revealed a strong relationship between chlorophyll content and SPAD-502 readings, with an R2 of 0.95 and a correlation coefficient (r) of 0.975. The developed AutoML models outperformed all traditional models, yielding the highest values of the coefficient of determination in prediction (Rp2) for all vegetation indices (VIs). The combination of SVIs and CVIs achieved the best prediction accuracy with the highest Rp2 values ranging from 0.89 to 0.98, respectively. This study demonstrated the feasibility of spectral and color vegetation indices as estimators of chlorophyll content. Furthermore, the developed AutoML models can be integrated into embedded devices to control nutrient cycles in aquaponics systems.