Motion Planning and Remote Sensing Algorithms, Predictive Geospatial Modeling and Deep Learning Artificial Intelligence Tools, and Machine Perception and Image Recognition Technologies in the Blockchain-based Virtual Economy

As the core of artificial intelligence, image recognition technology has been widely used in various fields and has brought great convenience to modern life. This paper expounds the image recognition technology and its principle, analyzes the process of image recognition based on artificial intelligence algorithm, and studies the application path of image recognition technology based on artificial intelligence algorithm. Finally, the advantages of artificial intelligence image recognition and its future development are pointed out.

Background: With the rapid development of digital twinning technology, the compatibility of digital twinning technology to other technologies is continuously enhanced. It is because of this that the application of image recognition technology in digital twinning technology becomes a reality. However, the key technology of digital twin, the virtual-actual mutual control technology, is not mature enough, and the image recognition technology applied in digital twin technology also has the problem of coordinate system transformation, which becomes an important link of image recognition technology in digital twin. Methods: Based on the above two problems, we take the tangram splicing project as an example to realize a virtual-actual mutual control method, so that the digital twinning technology can be well presented. Furthermore, we implement an image recognition applied to the conversion of digital twinning technology, so that digital twinning technology and image recognition have a seamless connection, allowing the application range of digital twinning technology to be further expanded. Results: In this paper, image recognition technology is successfully applied to digital twin technology by adopting the conversion between different coordinate systems and the real and virtual real time control, which makes the applicability of digital twin technology in high-tech fields such as smart factories and smart manufacturing to a higher level. Conclusions: Finally, through the tangram splicing project, the motion trajectories of the robotic arm and the tangram are consistent with the virtual robotic arm and the tangram in the computer. It is proved that our method can well combine digital twinning technology and image recognition technology.

The implementation of image recognition technology can significantly enhance the levels of automation and intelligence in smart agriculture. However, most researchers focused on its applications in medical imaging, industry, and transportation, while fewer focused on smart agriculture. Based on this, this study aims to contribute to the comprehensive understanding of the application of image recognition technology in smart agriculture by investigating the scientific literature related to this technology in the last few years. We discussed and analyzed the applications of plant disease and pest detection, crop species identification, crop yield prediction, and quality assessment. Then, we made a brief introduction to its applications in soil testing and nutrient management, as well as in agricultural machinery operation quality assessment and agricultural product grading. At last, the challenges and the emerging trends of image recognition technology were summarized. The results indicated that the models used in image recognition technology face challenges such as limited generalization, real-time processing, and insufficient dataset diversity. Transfer learning and green Artificial Intelligence (AI) offer promising solutions to these issues by reducing the reliance on large datasets and minimizing computational resource consumption. Advanced technologies like transformers further enhance the adaptability and accuracy of image recognition in smart agriculture. This comprehensive review provides valuable information on the current state of image recognition technology in smart agriculture and prospective future opportunities.

With the rapid development of technology, artificial intelligence is gradually applied in the field of image processing. Traditional image processing and recognition takes human extraction of features as a means to describe and characterize low-level edge information in images, but a large number of features extraction causes certain troubles to image recognition, and it is difficult to extract deep-level information. In recent years, deep learning, as an important emerging technology in the field of image recognition, has broad application prospects, and the research on image recognition technology has important theoretical value and practical significance to promote the development of computer vision and artificial intelligence. It opens up a whole new direction in image processing research, thus meeting the demand for efficient image processing. This paper reviews the applications of deep learning in image recognition, analyzes the classical convolutional neural network model, and summarizes the applications of deep learning in the fields of license plate recognition, face recognition, and medical image recognition.

With the rapid development of image recognition technology, more and more industries are benefiting from it, and the power industry is no exception. With the empowerment of image recognition technology, it can save human and material resources and improve digitalization. Meanwhile, digital twin technology is also being applied in various traditional industries. Digital twin technology and image recognition technology can promote each other and complement each other to achieve improved quality and efficiency. It is in this context that this paper discusses how image recognition technology and digital twin technology can contribute to the intelligent development of the electric power industry.

The existing intelligent recognition methods have the problem of fuzzy characteristics of digital archives, resulting in slow recognition speed. A digital archives intelligent recognition method based on image recognition technology is designed. Optimize the application and installation in power business expanding, quantify the information flow, select the attribute with the maximum information gain rate as the new node of the decision tree, extract the digital file features, construct the signaling recognition layer of power supply enterprises with image recognition technology, find the port mapping table, and formulate the intelligent recognition mode according to the linear summation function attribute.

with the continuous progress of the society, in recent years, the image recognition technology has a rapid development, whose new application fields are expanding. The intelligent automotive electronic information system and traffic monitoring research based on image recognition have become a new means to solve some social problems. At present, cars have become a necessity for many families, but at the same time, it has also brought a series of social problems, for example, the elderly and children were locked in the car and suffocated by the tragedy is often reported. In order to prevent these accidents, this paper proposes an intelligent vehicle-mounted/maintenance alarm system based on image recognition. Using deep learning algorithm of system main control board as the carrier, the system connects the temperature, pressure and CO2 concentration sensor and camera equipment, with the aid of technologies such as 4G communication technology, image recognition, automatically monitoring in the car, when there were something abnormal, it would timely alarm to remind the owner, and start the ventilation system of emergency handling, in order to solve corresponding problems.

Image recognition technology is an important part of machine vision applications. In the process of image recognition, the perception mechanism must exclude the input redundant information and extract important information. Loess is rich in calcium carbonate, and its particles are mainly composed of silt soil with obvious pores between soil particles. Loess in the basin is characterized by high water content, easy collapse, easy collapse, and strong permeability. Loess is very vulnerable to the influence of water, which greatly reduces the support of loess. Most of the quality problems of loess foundation-related projects in the basin are caused by insufficient support. In this paper, the loess in the watershed is selected as the investigation object. Firstly, in order to investigate the change of mechanical properties of loess, freezing thawing cycle, alternation of drying and wetting, and non-direct shear and compaction tests were carried out under these combined effects. Then, combined with the numerical simulation software geo-studio, the effects of temperature field, infiltration field, freezing and melting cycle, and wetting and drying alternation on the slope stability were simulated. Aerobic exercise is helpful to maintain the physiological health of the subjects; the results show that aerobic exercise with small intensity, moderate intensity, and medium intensity has a positive effect on the inhibition and conversion function of athletes' brain; aerobic exercise of three intensities can promote the development of brain refresh function; the results show that aerobic exercise of small plus medium intensity and small plus intensity has a positive effect on the brain activation of switching and inhibition function when athletes complete cognitive tasks. Based on the image recognition technology, this paper discusses the mechanical properties of loess in the basin and studies the monitoring of aerobic exercise data of athletes.

Microbial contamination of water sources is a pressing global challenge, disproportionately affecting developing regions with inadequate infrastructure and limited access to safe drinking water. In the Global South, waterborne pathogens such as bacteria, viruses, protozoa, and helminths contribute to diseases like cholera, dysentery, and typhoid fever, resulting in severe public health burdens. Predictive modeling emerges as a pivotal tool in addressing these challenges, offering data-driven insights to anticipate contamination events and optimize mitigation strategies. This review highlights the application of predictive modeling techniques-including machine learning, hydrological simulations, and quantitative microbial risk assessment -to identify contamination hotspots, forecast pathogen dynamics, and inform water resource allocation in the Global South. Predictive models enable targeted actions to improve water safety and lower the prevalence of waterborne diseases by combining environmental, socioeconomic, and climatic factors. Water resources in the Global South are increasingly vulnerability to microbial contamination, and the challenge is exacerbated by rapid urbanization, climate variability, and insufficient sanitation infrastructure. This review underscores the importance of region-specific modeling approaches. Case studies from sub-Saharan Africa and South Asia demonstrated the efficacy of predictive modeling tools in guiding public health actions connected to environmental matrices, from prioritizing water treatment efforts to implementing early-warning systems during extreme weather events. Furthermore, the review explores integrating advanced technologies, such as remote sensing and artificial intelligence, into predictive frameworks, highlighting their potential to improve accuracy and scalability in resource-constrained settings. Increased funding for data collecting, predictive modeling tools, and cross-sectoral cooperation between local communities, non-governmental organizations, and governments are all recommended in the review. Such efforts are critical for developing resilient water systems capable of withstanding environmental stressors and ensuring sustainable access to safe drinking water. By leveraging predictive modeling as a core component of water management strategies, stakeholders can address microbial contamination challenges effectively, safeguard public health, and contribute to achieving the United Nations' Sustainable Development Goals.

The pressure-holding sampling technology for deep-sea sediments has been identified as a prerequisite for studying the sedimentary environment with respect to the mechanical and biological characteristics of sediments. Thus, in this study, we designed a set of abyssal sediment sampling systems based on image recognition technology. First, a system camera was used to obtain the movement trajectory of the sampling cone in the laboratory simulation scene. After collecting the data of the image including the sampler, we preprocessed the data using an underwater image restoration method based on the dark channel prior. An image recognition unit was used to verify that the position of the sampling cone meets the requirement. The main control unit controlled the actuator of the sampling device to accurately control the position of the sampling cone. Furthermore, we used the Mask R-convolutional neural network architecture to build the network framework for controlling the expansion and braking of the sampler. The experimental results showed that the system achieved a very high detection accuracy for the position of the sampling cone.

The measurement of heart rate (HR) has numerous applications in various fields, such as the internet of things, security, sports, and telemedicine. There are many methods for measuring pulse rates, and this research is based on a novel technique of measuring the heartbeat using image recognition technology. The innovations in the field of visual objects have made the detection process easy and quick, with high efficiency. Four step-based algorithms, including a computer, an external high-definition camera, and an open-source computer vision library, have been presented for measuring heart rate. The first step was the face detection (FD) algorithm, and the second was the area attention algorithm to determine the region of interest (ROI). The ROI signal analysis algorithm was used in the third step, using a fast Fourier transform (FFT) for frequency detection. The pulse measurement phase was the final step, and it was based on the strength of the color concentration in proportion to the time extracted from video clips. With the help of our recorded database of 50 participants based on different ages and skin colors, the process was carried out. The results of this study contributed to the development of an HR detection technique based on image recognition using the Python programming language. This is a very comfortable and effective method for measuring the human heart rate. This research article discussed various factors and obstacles that affect heart rate measurement. The results found that our system is highly competent in measuring heart rate.

With the full popularity of China's railwayization process, it has brought about the problem of the management ability of railway traffic safety. Railway traffic safety emergency management capabilities are low. When an accident occurs, clearer data cannot be obtained in the first time to have a general understanding of the accident. Therefore, the problem of organizing rescue has always plagued relevant railway workers. This study aims to study the improvement of railway traffic emergency management based on image recognition technology in the context of big data. To this end, this study proposes image recognition technology based on deep learning, and through the relayout of the railway traffic emergency management system, so that the railway traffic problems can be dealt within time as soon as they occur, and designed an experiment to explore the ability of image recognition. The results of the experiment show that the efficiency of the improved railway traffic emergency management system has increased by 27%, and the recognition capability has increased by 64%. It can very well help current railway workers to carry out emergency management for railway traffic safety.

With the increase of power load, power infrastructure construction projects are also increasing year by year, and it is of great significance to strengthen the management of power infrastructure construction projects to ensure the safety and compliance of construction sites. In order to better warn the risks of power infrastructure construction sites, artificial intelligence image recognition and neural network technology can be introduced, and the principles of image recognition and neural network technology and the implementation methods in the early warning system of power infrastructure construction are analyzed in detail. By applying the early warning system for power infrastructure construction, construction risks can be well controlled, and the safety of power construction can be improved.

Research on innovation and application of 5G using artificial intelligence-based image and speech recognition technologies

With the exponential improvement of the integration capabilities of electronic hardware devices, digital images have become an indispensable information carrier, thus promoting the development of image recognition, detection, or tracking technologies. At present, there is a blank in the field of image recognition measured by the action standard of yoga standing three-dimensional movement, which will bring huge room for researchers to play. Based on the traditional image recognition technology, there are cases where missed detection or false detection cannot be correctly identified and made a correct judgment. In this paper, the ability to capture the action is improved by optimizing the network for the positioning of the stereoscopic action target of the yoga station. The linear discriminant analysis (LDA) after adding optimization is used to reduce the dimensionality of the captured image data, which is beneficial to improving image recognition rate and reducing image loss rate. Through the analysis of the three-dimensional movement of the yoga station, the model based on the improved algorithm is compared with the traditional model, and the final image recognition accuracy is improved compared with the network model before the improvement. The image recognition error rate steadily tends to 5%, and the loss rate is also below 2%. Through the optimized convolutional neural network, this paper can accurately capture the image position, and the recognition rate has also been greatly improved, which can provide a reference for future research in other fields.