The escalating global challenges of nutrient inefficiency and abiotic stress severely constrain agricultural productivity and sustainability. Nanofabricated fertilizers (NFFs) have emerged as a promising innovation, revolutionizing plant nutrition through precision nutrient delivery, enhanced bioavailability, and improved stress tolerance. This review elucidates recent advancements in the design, synthesis, and applications of NFFs, emphasizing their role in mitigating drought, salinity, and heavy metal stress in crop plants. The mechanisms underlying controlled nutrient release, uptake, and translocation to different plant tissues are discussed alongside their interactions with soil-plant-microbe systems. Special attention is given to the synergistic benefits of NFFs in improving nutrient use efficiency, modulating antioxidant defense systems, and sustaining soil health. Furthermore, potential environmental risks, biosafety concerns, and regulatory frameworks are critically assessed. The integration of nanofertilizers with artificial intelligence- and Internet of Things-driven precision agriculture platforms is highlighted as a transformative approach toward climate-resilient and resource-efficient food systems. Finally, key research priorities and future directions are outlined to facilitate the safe and scalable deployment of NFFs in sustainable crop production.

In the era of frequently changing global climatic conditions, abiotic stresses represent the primary constraints to global crop productivity which pose serious challenges to food security. Among the molecular factors implicated in stress adaptation, helicases involved in DNA and RNA metabolism have recently gained attention as potential targets for enhancing stress tolerance in various crop plants. SUV3 (Suppressor of Var3), a mitochondrial RNA helicase (mtSUV3), plays a critical role in mitochondrial RNA processing and maintenance. However, its involvement in abiotic stress responses remains largely unexplored. In this study, we generated marker-free transgenic rice (Oryza sativa L. cv. IR64) overexpressing mtSUV3 (mtSUV3-OE) and evaluated its functional role in salinity tolerance. Three independent marker-free transgenic rice lines were established and subjected to high salinity stress (200mM NaCl). Compared to wild-type plants, mtSUV3-OE lines exhibited enhanced tolerance to salt stress. Stable integration and expression of transgene were confirmed by PCR, Southern blot and RT-PCR analyses. Under salt stress conditions, transgenic lines accumulated significantly higher levels of phytohormones (IAA, GA and zeatin) and endogenous sugars (glucose and fructose). Moreover, mtSUV3-OE plants displayed improved photosynthetic performance, as evidenced by increased chlorophyll content, intercellular CO₂ concentration, stomatal conductance and net photosynthetic rate. Enhanced activities of antioxidant enzymes in transgenic lines correlated with reduced oxidative stress markers, including malondialdehyde and H₂O₂ content. Collectively, these findings uncover a previously uncharacterized role for mtSUV3 in mediating salinity tolerance in rice. Marker-free overexpression of mtSUV3 has the potential to enhance rice salinity tolerance. It also addresses biosafety issues and lays the groundwork for future field testing.

Calcium (Ca2⁺) signaling plays a pivotal role in plant defense responses against pests and pathogens, serving as an early and highly coordinated intracellular messenger. Upon biotic stress perception, Ca2⁺ channels mediate rapid Ca2⁺ influx, triggering downstream signaling pathways that activate defense-associated genes, secondary metabolite production, and hormonal pathways. This review explores the molecular mechanisms underlying Ca2⁺ channel activation, the spatiotemporal dynamics of intracellular Ca2⁺ fluxes, and their crosstalk with phytohormones and other signaling pathways. We further highlight the role of key Ca2⁺ sensors, such as calmodulins (CaMs), calcineurin B-like proteins (CBLs) and CBL-interacting protein kinase (CBL-CIPK) complexes, and calcium-dependent protein kinases (CDPKs) in decoding Ca2⁺ signals. In addition to this, emerging biotechnological approaches to enhance Ca2⁺-mediated resistance in crops like Nicotiana tabacum, Zea mays, and Arabidopsis thaliana has also been discussed. Understanding these mechanisms will provide valuable insights for developing new strategies to enhance plant resilience against evolving pest and pathogen threats under changing environmental conditions.

The complex transition from juvenile stage to maturity in plant’s life cycle consists of development, reproduction, and senescence of its primary organs and involves various molecular processes. Ethylene and polyamines (PAs) along with other plant hormones play a crucial role in promoting various signals and enabling the emergence of conditions that are conducive to stage development, and successful reproduction. Additionally, during their entire lifespan, plants encounter several environmental challenges and to counteract such adverse conditions, they develop defense strategies that are regulated by phytohormones. The longevity of plants is either directly or indirectly regulated by shifts in the levels of ethylene and PAs, their perception, and crosstalk. As PAs deficiency or overabundance might be harmful to cell survival, thus PAs homeostasis must be strictly regulated. Plants regulate PAs biosynthesis, catabolism and its transport to maintain homeostasis. Present study focused on biosynthesis and signaling of ethylene and PAs, and their role in plant growth at different developmental stages. An attempt is made to fill in the knowledge gaps and offer a critical evaluation of current research on function of ethylene and PAs in plant development and stress responses. Knowing how ethylene and PAs regulate the growth of plants have important implications for agriculture and sustainable growth.

Nutritional deficiencies in crops lead to significant yield losses. Early and accurate detection of these deficiencies is crucial for effective intervention, as it enables timely corrective measures, minimizes crop damage, and ensures optimal productivity. Traditional methods for identifying deficiencies generally rely on manual inspection of the leaves, which is time-consuming and prone to errors. To address this challenge, we propose a deep learning (DL)-based approach that uses the latest advancements in object detection model YOLOv8 to detect and locate the affected tomato leaves. This paper presents a DL approach for detecting nutritional deficiencies in tomato leaves using the YOLOv8 model and a layered augmentation scheme. The augmented dataset allows the model to identify subtle signs of deficiencies better, leading to more accurate predictions. Our method was evaluated based on key performance metrics such as accuracy, memory usage, and mAP50. The model achieved an mAP@0.50 of 92.7 % and an mAP@0.50-0.95 of 89.1 %, with outstanding precision of 89.1 %, recall rate of 83.1 %, and a balanced F1 score of 89.5. The results demonstrate that the proposed framework achieves superior performance in detecting nutritional deficiencies when compared to earlier models in terms of mAP, accuracy precision, recall and F1 score. The combination of advanced object detection capabilities and an augmented dataset makes this approach valuable for precision agriculture, enabling timely and targeted interventions to improve crop health and productivity. Also, an Android app has been developed for real-time application of the approach.

Superoxide dismutase (SOD), a metalloenzyme, catalyses the dismutation of superoxide anions (O2•‾) into molecular oxygen (O2) and hydrogen peroxide (H2O2), perform crucial roles in plant growth, development, and responses to multiple abiotic stressors. Present study attempted to explore the SOD gene family in chickpea and their key role in salinity and drought tolerance. Computational analysis of SOD gene family in chickpea revealed 10 SODs (4 Cu/ZnSODs and 6 Mn/FeSODs) and explored their chromosomal location, evolutionary relationships, structure, conserved motifs, promoter analysis, tissue specific expression analysis, protein-protein interactions and docking of CaSODs with their predicted interacting partners. GO (gene ontology) and KEGG analysis revealed association of CaSODs in ROS signalling, metal binding, and catalysis, which contribute in stress tolerance and cellular homeostasis. Further, transcriptomic analysis revealed that CaSODs showed differential expression pattern under salinity and drought conditions. qRT-PCR was performed to analyse the response of CaSODs in salinity ICCV2 (tolerant), JG62 (susceptible) and drought ICC4958 (tolerant), ICC1882 (susceptible) genotypes. A comparative analysis of gene expression in ICCV2, JG62, ICC4958 and ICC1882 revealed number of CaSODs, such as CaCSD3, CaCSD2, and CaCSD4, showed high expression in response to salinity and drought stress, suggesting their involvement in stress response pathways as predicted by GO analysis. miRNA analysis revealed that CaCSDs and CaMSDs were targeted by miRNAs (CaCSD4-miR398a/b/c, and CaMSD-miR747). Additionally, the study found SNP variation in two CaSODs (CaMSD5 and CaMSD6) promoter regions, which could affect expression pattern of these genes. Our findings provide the basis to understand the functional roles of CaCSD3/CaCSD4 in salinity tolerance and CaCSD3 for drought tolerance by reducing oxidative stress, offer important information for future research with the objective of improving chickpea stress tolerance using breeding or genetic engineering technologies.

Bitter gourd is an important cucurbitaceous vegetable widely grown in India and other tropical and subtropical regions and appreciated for its nutritional, medicinal, and economic values. Traditional way of detecting diseases and nutrient deficiencies in bitter gourd leaves requires significant effort and expertise whereas, precision farming and automated disease detection methods can greatly support farmers by facilitating sustainable agriculture To address this challenge a novel web based application AgriCure was developed which incorporated a multilevel approach to detect the plant disease and nutrient deficiency with high level. It uses a hybrid augmentation-based YOLOv8 DL model for image analysis. The study focuses on detecting diseases like Downy Mildew, Leaf Spot, and Jassid, as well as nutrient deficiencies such as Potassium, Magnesium, and Nitrogen Deficiency and their combinations. The initial dataset of 785 images was increased to 2430 images using advanced data augmentation. The results on the augmented dataset after 100 epochs demonstrated high effectiveness with the augmented dataset. The model achieved an impressive mean Average Precision (mAP50) of 92.9 % at an Intersection over Union (IoU) threshold of 0.50 and a mAP50-95 of 91.5 % across IoU thresholds from 0.50 to 0.95. Nearly all predicted positive instances were true positives, with a precision rate of 89.6 % and a recall of 86.6 %, which showed the capacity of the model in identifying true positives. The F1 score of 91.66 % highlighted balanced performance of the model between precision and recall, emphasising its reliability and accuracy. The model shows low losses, with a Box loss of 0.2435, a Class loss of 0.1689, and a Distribution Focal Loss (dfl loss) of 0.9024. This approach offered a valuable tool for early and accurate detection of disease and nutrient deficiency. Detection results indicate that, compared to previous methods, the proposed approach significantly improves overall performance and addresses challenges tied to limited dataset sizes.

Plant development and productivity are significantly hindered by salt stress, leading to substantial financial losses in the agriculture sector. Salinity stress negatively impacts the overall growth, physiology, and metabolism of plants. Specifically, NaCl stress is particularly harmful to tomato plants, causing suppression of seedling growth, accumulation of sodium (Na+) and chloride (Cl-) ions, disrupted ion homeostasis, reduced proline and chlorophyll content, and impairment of antioxidant enzyme systems. This research aimed to investigate the role of exogenous putrescine (PUT) application on tomato (Solanum lycopersicum L.) seedlings under NaCl stress (250 mm) to determine its potential protective effects. Various physio-biochemical attributes were estimated using precise protocols for NaCl-treated, PUT-treated, and untreated controlled tomato seedlings also analyzed for the expression of ACS1, NHX1, HKT1;2, and SOS1 genes. Additionally, ACC synthase activity, ethylene content, electrolyte leakage, proline content, Na+ and potassium (K+) ion content, lycopene content, and antioxidant enzyme activities were examined. Results indicated that PUT application enhanced the expression of ACS1, NHX1, HKT1;2, and SOS1 genes increase the ACC synthase activity, ethylene content, proline content, and Na+ and K+ ion content, while reducing electrolyte leakage. Furthermore, PUT application significantly increased the activity of superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), and glutathione reductase (GR), as well as other morphological parameters. Overall, our research demonstrated the potential benefits of PUT applications for enhancing crop growth and improving salt stress tolerance, which are crucial for agronomy.

The quality of horticultural crops is significantly crucial for agricultural yield because of market demand, quality, and the priority of consumers. Macronutrients like nitrogen (N) and potassium (K) are crucial for the normal growth and development of crops. Thus, detecting nutritional deficiency in eggplant is very important for ensuring optimal growth and yield. The traditional approaches are time-consuming and require expert knowledge. The previously reported research in eggplant with a deep learning (DL) approach targeted disease detection and classification work. No work has been reported on eggplant nutritional deficiency detection using the genetic algorithm (GA) based tuning approach with data augmentation. This paper presents a YOLOv9 deep-learning model, optimized with a GA to find the best hyperparameters and data augmentation techniques to increase its robustness. The study used the OLID I dataset to detect nutritional deficiencies in eggplant leaves. The experimental results show that our approach achieved an accuracy of 94.52 %, mAP50 of 94.55 %, mAP50-95 of 93.23 %, Precision of 95.9 %, Recall of 92.8 %, and F1 Score of 94.32 %. These results suggest that the proposed approach is a significant step towards developing a practical application to support farmers in detecting nutrition deficiencies in the eggplant crop.

Tomato is a key crop in global agriculture, yet it faces yield and quality challenges due to various diseases. Traditional disease identification methods are slow and require expertise, limiting their practicality in large-scale farming. Integrating automated disease detection with precision agriculture provides a timely, accurate diagnosis, promoting sustainable practices. However, the scarcity of real-world data hampers effectiveness. To address this issue, data augmentation techniques simulate variations in farm images, enriching datasets for improved detection of diseases. This investigation aims to identify seven different tomato diseases, such as bacterial spot, early blight, late blight, and others, while also detecting healthy plant leaves. Unlike previous studies that relied on the controlled PlantVillage dataset, this study utilizes the real-world PlantDoc dataset. The study addresses different challenges faced throughout the model development process, like data scarcity and imbalances. A hybrid data augmentation technique is introduced to increase the dataset size from 737 images to 6696 images, which improves the accuracy and robustness of the computer vision model. The study employs the YOLOv8n deep convolutional neural network, achieving 96.5% mAP, 97% precision, 93.8% recall, and 95% F1 score. The results demonstrate a significant improvement in disease detection, addressing challenges from inadequate datasets and advancing AI-driven precision agriculture. The proposed YOLOv8n model has the potential to be applied beyond its current scope by training it on datasets of other crops. The model can learn and generalize the unique image features associated with various crop types, expanding its utility in agricultural applications. This flexibility allows the model to detect and classify plant characteristics, diseases, or pests across different crops, enabling its use in diverse agricultural environments. As a result, the YOLOv8n model could serve as a robust tool for precision farming, helping to optimize crop management and enhance productivity on a broader scale.

Plant signaling and stress response systems depend heavily on the essential functions of heterotrimeric G-proteins, mitogen-activated protein kinases (MAPKs), and helicases. Researchers have thoroughly investigated each molecular component separately but still lack comprehensive knowledge about how they work together functionally. This review investigates the interactions between G-proteins, MAPKs, and helicases as fundamental components of plant stress signaling networks. G-proteins function as molecular switches that perceive stress signals to initiate downstream cascades which activate MAPK pathways. MAPKs trigger phosphorylation of vital target proteins such as transcription factors and helicases which in turn regulate gene expression and RNA metabolism. Helicases, crucial for plant stress response mechanisms, unwind nucleic acid structures. Recent research shows that MAPKs and helicases together manage ribosome loading along with mRNA stability and protein production when plants face environmental stress. The review examines molecular interactions that provide new insights into plant stress physiology, while highlighting the need for further investigation into plant adaptive mechanisms involving G-proteins, MAPKs, and helicases.

Cotton production is a crucial agricultural industry, a raw material source for the textiles sector and a major source of livelihood for more than 30 million farmers globally. The yield and quality of cotton (Gossypium) are influenced by different types of stress and diseases. Deep Learning as a solution for disease prevention, detection, and management can increase the yield, reduce the cost and improve the quality of crop. This study presents a robust method using 10-fold cross-validation with the YOLOv8 DL model for precise cotton leaf disease recognition. The k-fold cross-validation mitigates overfitting by training the model on diverse data subsets, which leads to enhanced generalizability while ensuring reliable performance. The proposed method achieved 99.60% and 100% as Top_1 and Top_5 accuracy, respectively. The method also achieved a recall of 99.53%, a precision of 99.53%, and an F1 score of 99.60%. During 10 trials, the method consistently performed with an average. Top_1 and Top_5 accuracy of 98.41% and 100% respectively, recall 98.53%, precision 98.39% and F1 score 98.42%.This study is among the first to apply YOLOv8 classification with 10-fold cross-validation for multi-class cotton leaf disease identification using field-captured images.

G protein-coupled receptors (GPCRs) constitute the largest family of transmembrane proteins in metazoans that mediate the regulation of various physiological responses to discrete ligands through heterotrimeric G protein subunits. The existence of GPCRs in plant is contentious, but their comparable crucial role in various signaling pathways necessitates the identification of novel remote GPCR-like proteins that essentially interact with the plant G protein α subunit and facilitate the transduction of various stimuli. In this study, we identified three putative GPCR-like proteins (OsGPCRLPs) (LOC_Os06g09930.1, LOC_Os04g36630.1, and LOC_Os01g54784.1) in the rice proteome using a stringent bioinformatics workflow. The identified OsGPCRLPs exhibited a canonical GPCR ‘type I’ 7TM topology, patterns, and biologically significant sites for membrane anchorage and desensitization. Cluster-based interactome mapping revealed that the identified proteins interact with the G protein α subunit which is a characteristic feature of GPCRs. Computational results showing the interaction of identified GPCR-like proteins with G protein α subunit and its further validation by the membrane yeast-two-hybrid assay strongly suggest the presence of GPCR-like 7TM proteins in the rice proteome. The absence of a regulator of G protein signaling (RGS) box in the C- terminal domain, and the presence of signature motifs of canonical GPCR in the identified OsGPCRLPs strongly suggest that the rice proteome contains GPCR-like proteins that might be involved in signal transduction.