In this paper, a highly sensitive graphene-based multiple-layer (BK₇/Au/PtSe₂/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing region. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θ_SPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimensional (2D) materials were used to achieve significant enhancement in the Goos-Hänchen (GH) shift detection sensitivity and plasmonic properties of the conventional SPR sensor. The proposed sensor successfully senses the COVID-19 virus and offers additional (1 + 0.55) × L times sensitivity owing to the added graphene layers. Besides, the performance of the proposed sensor was analyzed based on detection accuracy (DA), the figure of merit (FOM), signal-noise ratio (SNR), and quality factor (QF). Based on its performance analysis, it is expected that the proposed sensor may reduce lengthy procedures, false positive results, and clinical costs, compared to traditional sensors. The performance of the proposed sensor model was checked using the TMM algorithm and validated by the FDTD technique.

As the world’s population rises, there will be a greater need for food, which will have repercussions on the environment and on crop yields. Increased production, efficient resource allocation, climate change adaptation, and diminished food waste are the four cornerstones of Agriculture 4.0’s vision for the future of farming. Agriculture 4.0 makes use of cutting-edge data systems and Internet technology to acquire, analyze, and organize massive amounts of farming facts such as weather reports, soil conditions, market demands, and land usage to better guide farmers’ decisions and boost their bottom lines. As a result, research on agricultural decision support systems for Agriculture 4.0 has gained significant momentum. Crop monitoring and yield forecasting are two applications where remote sensing has proven useful, and these two areas are intrinsically linked to variations in soil, weather, and biophysical and biochemical factors. Multi- and hyper-spectral data, radar, and lidar imaging are just some of the remote tools that could be employed for crop monitoring and yield forecasting. This paper’s goal is to examine some of the difficulties that can arise in the future while using agricultural decision-support platforms in the context of Agriculture 4.0. Addressing these identified obstacles may help future researchers create better decision-assistance systems. This research examines the possibilities, benefits, and drawbacks of each method, as well as how well they work in various agricultural settings. Furthermore, these methods are demonstrated in a variety of strategies that can be effectively employed. In this research, we take a look at some remote sensing techniques developed to increase farm profits while minimizing their impact on the natural world. This research shows how remote sensing information can be used to predict crop yields, evaluate plant nutrient needs and soil nutrient levels, calculate plant moisture levels, and manage weed populations, among other applications.