Remote sensing (RS) techniques offer advantages over other methods for measuring soil properties, including large-scale coverage, a non-destructive nature, temporal monitoring, multispectral capabilities, and rapid data acquisition. This review highlights the different detection methods, types, parts, and applications of RS techniques in soil measurements, as well as the advantages and disadvantages of the measurements of soil properties. The choice of the methods depends on the specific requirements of the soil measurements task because it is important to consider the advantages and limitations of each method, as well as the specific context and objective of the soil measurements, to determine the most suitable RS technique. This paper follows a well-structured arrangement after investigating the existing literature to ensure a well-organized, coherent review and covers all the essential aspects related to studying the advancement of using RS in the measurements of soil properties. While several remote sensing methods are available, this review suggests spectral reflectance, which entails satellite remote sensing and other tools based on its global coverage, high spatial resolution, long-term monitoring capabilities, non-invasiveness, and cost effectiveness. Conclusively, RS has improved soil property measurements using various methods, but more research is needed for calibration, sensor fusion, artificial intelligence, validation, and machine learning applications to enhance accuracy and applicability.

A few years back, when the image processing hardware and software were created, it was limited, and most of the time, object detection would fail., But as with time, the advancement in technology has significantly changed the scenario. A lot of researchers worked in this field to carry out a solution through which they can detect objects in any field, especially in the robotic domain [1]. In today's world, with so much research in the field of deep learning, it is very easy to identify and detect any object using computer vision. This paper focuses on the various deep learning technologies and algorithms through which object detection can be done. A new and advanced deep learning technology known as salient object detection has been discussed. Also, the 3D object detection and the end-to-end approach for object detection are discussed. The existing methods of deep learning through which object detection can be done. The applications in which object detection is being used and the importance of object detection. It also reports; what the predecessors have done, what problems have been solved by them, how they solved these problems, the characteristics of the predecessors' methods and their future work.

Ammonia (NH${}_3$) represents a perilous gas that poses a substantial hazard to both human well-being and the environment, particularly within agricultural regions. Agricultural activities constitute a primary source of ammonia emissions. Thus, effective monitoring and measurement of ammonia sources in agriculture are imperative for mitigating its adverse impact. However, not all existing ammonia detection methods are suitable for discerning the low concentrations typically encountered in agricultural ammonia volatilizing (ranging from 0.01 to 5 parts per million). Consequently, curtailing ammonia volatilization from farmland assumes paramount importance, with real-time monitoring serving as a crucial mechanism for assessing environmental contamination and minimizing agricultural ammonia losses. Deploying appropriate detection methodologies ensures that requisite measures are taken to safeguard human health and the environment from the deleterious repercussions of ammonia exposure. The present paper introduces a comprehensive approach to detecting and analyzing ammonia in agricultural settings. It elucidates the merits and demerits of conventional indoor and outdoor ammonia detection methods, juxtaposing them with the innovative technology of Electronic nose (E-nose). Within the paper, seven widely employed ammonia detection methods in farmland are scrutinized and compared against traditional techniques. Additionally, the constructional aspects and distinct components of E-nose are meticulously delineated and appraised. Ultimately, the paper culminates in a comprehensive comparative analysis encompassing all the aforementioned methodologies, elucidating the potential and limitations of E-nose in facilitating ammonia detection endeavors within agricultural contexts.

Nowadays, the unmanned aerial vehicle (UAV) has a wide application in transportation. For instance, by leveraging it, we are able to perform accurate and real-time vehicle speed detection in an IoT-based smart city. Although numerous vehicle speed estimation methods exist, most of them lack real-time detection in different situations and scenarios. To fill the gap, this paper introduces a novel low-altitude vehicle speed detector system using UAVs for remote sensing applications of smart cities, forging to increase traffic safety and security. To this aim, (1) we have found the best possible Raspberry PI’s field of view (FOV) camera in indoor and outdoor scenarios by changing its height and degree. Then, (2) Mobile Net-SSD deep learning model parameters have been embedded in the PI4B processor of a physical car at different speeds. Finally, we implemented it in a real environment at the JXUST university intersection by changing the height (0.7 to 3 m) and the camera angle on the UAV. Specifically, this paper proposed an intelligent speed control system without the presence of real police that has been implemented on the edge node with the configuration of a PI4B and an Intel Neural Computing 2, along with the PI camera, which is armed with a Mobile Net-SSD deep learning model for the smart detection of vehicles and their speeds. The main purpose of this article is to propose the use of drones as a tool to detect the speeds of vehicles, especially in areas where it is not easy to access or install a fixed camera, in the context of future smart city traffic management and control. The experimental results have proven the superior performance of the proposed low-altitude UAV system rather than current studies for detecting and estimating the vehicles’ speeds in highly dynamic situations and different speeds. As the results showed, our solution is highly effective on crowded roads, such as junctions near schools, hospitals, and with unsteady vehicles from the speed level point of view.

This investigation is centrally focused on the comprehensive evolution and enhancement of FOODIEBOT, an adaptive service automaton with a wide range of functionalities. Its capabilities encompass sophisticated image processing methods, seamlessly integrated via mobile applications (APP) and web interfaces, tailored specifically for intricate object manipulation in dining hall settings.During its developmental phase, the precise calibration of PID controller coefficients emerged as an essential requirement. The model underwent meticulous scrutiny through detailed simulations using MATLAB software. Following this phase, its operational efficiency navigating through circular, elliptical, spiral, and octagonal trajectories underwent rigorous examination, utilizing optimization methodologies like BAS, PSO, POA, and EO. The exposition emphasizes the diverse dispersion of optimized coefficients within each algorithmic framework. The pinnacle of this effort involved a comprehensive evaluation of pathway performance, amalgamating insights from each optimization paradigm. The discussion extensively delineates both simulated and real-time performance metrics of the robot, validating the accuracy and reliability of simulation in deriving PID controller values.In the comprehensive evaluation of methodologies and the robotic system’s effectiveness, the BAS technique excels in operational efficiency. This method consistently outperforms its counterparts in execution time, primarily due to its meticulous optimization of particle count. The comparative analysis across various trajectories reveals intriguing insights. The EO approach showcases outstanding accuracy in Path 1, while the POA method achieves optimal precision in Path 3. Impressively, the BAS technique demonstrates unparalleled accuracy in Path 4. Furthermore, in terms of solution optimization, the BAS method consistently displays the shortest execution times across all traversed pathways. When examining maximum velocity along these routes, the PSO method excels in Paths 1, 3, and 4, consistently achieving the highest speeds. Notably, Path 2 uniquely displays the peak velocity attained by the POA method.This article presents comprehensive insights into the constituent elements of the robotic system’s design. The inquiry delves into the intricate nuances of optimization methodologies, elucidating their profound impact on the service automaton’s performance across diverse orientations. The pragmatic implications underscore the critical role of temporal considerations in the judicious selection of these methodologies. The observed congruence between simulated and practical performance serves as a definitive validation, affirming the precision of simulation computations and the subsequent derivation of PID controller values.

This research is mainly aimed at determining the effect of renewable energy (RE), education expenditures, and CO₂ emissions on health expenditures in selected South Asian countries. There is an insufficient number of studies that investigate the linkages between health expenditures (HE) and CO₂ emissions in South Asian countries. This study combined RE and gross domestic product (GDP) to identify their effect on health spending. We utilized the annual data of 1990–2018, and applied FMOLS and DOLS estimators over the panel data of five South Asian countries. According to the DOLS and FMOLS long-run results, GDP, RE, and education expenditures are negatively associated with health expenditures. This suggests that renewable energy puts less pressure on environmental quality, which leads to less health spending in the five South Asian countries studied. The empirical results also show that HE and CO₂ emissions are positively and significantly related, which implies that an increase in CO₂ emissions increases the financial burden on the various countries’ health sector. This study, therefore, recommends the usage of renewable sources to improve public health and to help lower health expenditures. To achieve sustainable development, it is also important to increase investment in the educational sector in the various countries.