Micronutrients play a prominent role in plant growth and development, and their bioavailability is a growing global concern. Zinc is one of the most important micronutrients in the plant life cycle, acting as a metallic cofactor for numerous biochemical reactions within plant cells. Zinc deficiency in plants leads to various physiological abnormalities, ultimately affecting nutritional quality and posing challenges to food security. Biofortification methods have been adopted by agronomists to increase Zn concentrations in crops through optimal foliar and soil applications. Changing climatic conditions and conventional agricultural practices alter edaphic factors, reducing zinc bioavailability in soils due to abrupt weather changes. Precision agriculture emphasizes need-based and site-specific technologies to address these nutritional deficiencies. Nanoscience, a multidimensional approach, reduces particle size to the nanometer (nm) scale to enhance their efficiency in precise amounts. Nanoscale forms of Zn⁺² and their broad applications across crops are gaining attention in agriculture under varied application methods. This review focuses on the significance of Zn oxide (ZnO) nanoparticles (ZnONPs) and their extensive application in crop production. We also discuss optimum dosage levels, ZnONPs synthesis, application methods, toxicity, and promising future strategies in this field.

International Journal of Current Microbiology and Applied Sciences ISSN:2319-7692(Print), ISSN:2319-7706(Online) is a multidisciplinary peer-reviewed journal with reputable academics and experts as members of its Editorial Board. The Current Microbiology aims to publish all the latest and outstanding research articles. Review and letters in all areas of major importance to techniques of microbiology and applied research with publishes high quality of review and research articles on novel aspects of Microbiology including Environmental, Food, Agricultural, Medical, Pharmaceutical, Veterinary, Soil, Water and Biodeterioration.

Plant disease management machinery coupled with sustainable agricultural development works to plant crops in the soils along with agriculture evolutionary force. Therefore, early and accurate identification of diseases is necessary, so able to minimize losses due to yield, chemical intervention, and produce safe quality agriculture goods. Traditional methods of disease diagnosis most often include visual examination by experts and may be time-consuming and cumbersome; moreover, they can be prone to error, especially under large-scale cultivation. However, the development in the imaging domain, together with proper computational analysis, can provide mechanistic methods of approach to detect diseases. Visual-based methods examine the various parts of the plant, such as leaves, stems, and fruits, for certain features. This allows for the evaluation of plant health to be done relatively quickly and in a nondestructive manner. On the other hand, the well-annotated datasets are still few and far between, different environmental conditions do change the way disease symptoms appear, and reliable systems are still difficult to set up in field conditions or where there is lack of technological infrastructure. If we consider things at a longer timeline, we might see the generation of huge amounts of high-quality datasets, the foaling of super-diagnostic models that can cover many crops and climates, and the coupling of detection methods with precision agriculture tools for continuous monitoring as the research focus. However, if the main barriers are eliminated, then the new methods for disease detection would greatly contribute to making the dreams of sustainable farming, efficient use of resources for crop management, and global food security come true.

The demand for high‐precision components in the aerospace, biomedical, and automotive industries has increased the use of nonconventional machining processes. Electric discharge machining (EDM) is widely used to machine hard materials, such as stainless steel 316, because it can achieve high dimensional accuracy. However, dimensional deviations and overcuts persist. To address these issues, a graphene‐mixed dielectric fluid was employed during machining. Experimental investigations were conducted using the Taguchi L27 orthogonal array design. The results demonstrated that the copper electrode produced superior dimensional accuracy compared with the brass electrode. ANN‐assisted MOGA optimization identified the optimal machining parameters as a peak current of 17.00 A, a pulse‐on time of 110.76 μ s, and a pulse‐off time of 88.02 μ s. These conditions are predicted to yield radial overcuts of 57.25 μ m and 74.63 μ m for the copper and brass electrodes, respectively. Confirmation experiments showed excellent agreement with the ANN predictions.