Nanotechnology is a burning field of scientific interest for researchers in current era. Diverse plant materials are considered as potential tool in green chemistry based technologies for the synthesis of metal nanoparticles (NPs) to cope with the hazardous effects of synthetic chemicals, leading to severe abiotic climate change issues in today’s agriculture. This study aimed to determine the synthesis and characterization of metal-based nanoparticles using extracts of the selected plant Calotropis gigantea and to evaluate the enzyme-inhibition activities and antibacterial and antifungal activity of extracts of metal-based zinc nanoparticles using C. gigantea extracts. The crystal structure and surface morphology were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). C. gigantea was examined for antimicrobial activity against clinical isolates of bacteria and fungi. The water, ethanolic, and acetone extracts of C. gigantea were studied for their antagonistic action against bacterial strains (E. coli, S. aureus, P. multocida, and B. subtilis) and selected fungal strains (A. paracistic, F. solani, A. niger, S. ferrugenium, and R. nigricans). In vitro antimicrobial activity was determined by the disc diffusion method, where C. gigantea was tested for AChE and BChE inhibitory activity using Ellman’s methodology. The kinetic analysis was performed by the proverbial Berthelot reaction for urease inhibition. The results showed that out of all the extracts tested, ethanolic and water extracts possessed zinc nanoparticles. These extracts showed the maximum zone of inhibition against F. solani and P. multocida and the lowest against S. ferrugenium and B. subtilis. A potential source of AChE inhibitors is certainly provided by the abundance of plants in nature. Numerous phyto-constituents, such as AChE and BChE inhibitors, have been reported in this communication. Water extract was active and has the potential for in vitro AChE and BChE inhibitory activity. The urease inhibition with flower extracts of C. gigantea revealed zinc nanoparticles in water extracts that competitively inhibited urease enzymes. In the case of cholinesterase enzymes, it was inferred that the water extract and zinc nanoparticles have more potential for inhibition of BChE than AChE and urease inhibition. Furthermore, zinc nanoparticles with water extract are active inthe inhibition of the bacterial strains E. coli, S. aureus, and P. multocida and the fungal strains A. paracistic, F. solani, and A. niger.

Silver nanoparticles have gained considerable interest in recent decades due to their antimicrobial activity and are used in water disinfection, wound healing, food packaging, and plant protection. This study tested the potential of silver nanoparticles synthesized using the neem (Azadirachta indica) leaf extract against Alternaria solani causes early blight disease in tomato plants. The pathogen was isolated from infected tomato plants and identified using morphological and molecular features. The results showed significant variation among isolates. Isolates, Shk-1 and Ksr-1 were highly pathogenic, causing up to 80% disease incidence. The potential of silver nanoparticles against each isolate was determined using different concentrations of silver nanoparticles. During in vitro and in vivo experiments, the growth inhibition rate of the pathogen was 70–100% at 50 ppm. Lower concentrations of silver nanoparticles (5 and 10 ppm) increased phenolics, PO, PPO, and PAL production by more than 50% as compared to the untreated control. These defensive mechanisms clearly demonstrate the fungicidal potential of AgNPs and recommend their utilization in different crop protection programs.

Rhizoctonia solani is one of the most common soil-borne fungal pathogens of legume crops worldwide. We collected rDNA-ITS sequences from NCBI GenBank, and the aim of this study was to examine the genetic diversity and phylogenetic relationships of various R. solani anastomosis groups (AGs) that are commonly associated with grain legumes (such as soybean, common bean, pea, peanut, cowpea, and chickpea) and forage legumes (including alfalfa and clover). Soybean is recognized as a host for multiple AGs, with AG-1 and AG-2 being extensively investigated. This is evidenced by the higher representation of sequences associated with these AGs in the NCBI GenBank. Other AGs documented in soybean include AG-4, AG-7, AG-11, AG-5, AG-6, and AG-9. Moreover, AG-4 has been extensively studied concerning its occurrence in chickpea, pea, peanut, and alfalfa. Research on the common bean has been primarily focused on AG-2, AG-4, and AG-1. Similarly, AG-1 has been the subject of extensive investigation in clover and cowpea. Collectively, AG-1, AG-2, and AG-4 have consistently been identified and studied across these diverse legume crops. The phylogenetic analysis of R. solani isolates across different legumes indicates that the distinct clades or subclades formed by the isolates correspond to their specific anastomosis groups (AGs) and subgroups, rather than being determined by their host legume crop. Additionally, there is a high degree of sequence similarity among isolates within the same clade or subclade. Principal coordinate analysis (PCoA) further supports this finding, as isolates belonging to the same AGs and/or subgroups cluster together, irrespective of their host legume. Therefore, the observed clustering of R. solani AGs and subgroups without a direct association with the host legume crop provides additional support for the concept of AGs in understanding the genetic relationships and evolution of R. solani.

Leaf rust is a devastating disease in wheat crop. The disease forecasting models can facilitate the economic and effective use of fungicides and assist in limiting crop yield losses. In this study, six wheat cultivars were screened against leaf rust at two locations, during three consecutive growing seasons. Subsequently, the stepwise regression analysis was employed to analyze the correlation of six epidemiological variables (minimum temperature, maximum temperature, minimum relative humidity, maximum relative humidity, rainfall and wind speed) with disease severity and yield loss (%). Disease predictive models were developed for each cultivar for final leaf rust severity and yield loss prediction. Principally, all epidemiological variables indicated a positive association with leaf rust severity and yield loss (%) except minimum relative humidity. The effectiveness of disease predictive models was estimated using coefficient of determination (R²) values for all models. Then, these predictive models were validated to forecast disease severity and yield loss at another location in Faisalabad. The R² values of all disease predictive models for each of the tested cultivars were high, evincing that our regression models could be effectively employed to predict leaf rust disease severity and anticipated yield loss. The validation results explained 99% variability, suggesting a highly accurate prediction of the two variables (leaf rust severity and yield loss). The models developed in this research can be used by wheat farmers to forecast disease epidemics and to make disease management decisions accordingly.

Cotton (Gossypium hirsutum) is a global cash crop which has gained importance in earning foreign exchange for each country. Bacterial blight caused by Xanthomonascitri subsp. malvacearum (Xcm) has been a seriousdisease in Pakistan’s cotton belt on multiple occasions. Bacterium was isolated and identified through various biochemical and diagnostic tests wherehypersensitivity reaction, Gram staining, KOH (potassium hydroxide), catalase, starch hydrolysis, lecithinase and Tween 80 hydrolysis tests confirmed bacterium as Gram-negative and plant pathogenic. Xcm perpetuation assays wereevaluated on various cotton varieties under glasshouse conditions in completely randomized design by three different methods, wherein the scratch method proved to be the best upon CIM-496 and showed 83.33% disease incidence as compared with the other two methods, where Bt-3701 responded with 53.33% incidence via the spray gun method, and 50% with the water splash method on CIM-616, as compared with the control. Similarly, for disease severity percentage, Bt-3701 was pragmatic with 47.21% through scratch method, whereas, in the spray gun method, 45.51% disease severity was noted upon Bt-802, and 31.27% was calculated on Cyto-179 through the water splash method. Owing to the unique antibacterial properties of aqueous plant extracts, the poison food technique showed Aloe vera, Mentha piperita, Syzygiumcumini and Azadirachta indica with 17.77, 29.33, 18.33 and 20.22 bacterial colonies counted on nutrient agarmedium petri plate, respectively, as compared with the control. Measurement of the inhibition zone by disk diffusion technique showed Mentha piperita, Syzygiumcumini, Citrus limon, Moringa oleifera and Syzygium aromaticum to present the most promising results by calculating the maximum diameter of the inhibition zone, viz., 8.58, 8.55, 8.52, 8.49 and 8.41 (mm), respectively, at the highest tested concentration (75 ppm, parts per million) compared with the control. It is probable that the decoction’s interaction with the pathogen population on the host plant will need to be considered in future experiments. However, at this moment, more research into the effective management of cotton bacterial blight by plant extracts in terms of concentration determination and development of biopesticides will provide future avenues to avoid environmental pollution.