A New IoT Gateway for Artificial Intelligence in Agriculture

The Internet of Things (IoT) has ushered in a new era of innovative agriculture research. Because IoT is still in its early stages, it must be widely tested before it can be widely deployed in many agricultural applications. In this paper, I look at different prospective IoT applications, as well as the unique concerns and constraints connected with IoT deployment for better farming. The gadgets and wireless communication technologies linked with IoT in agricultural and farming applications are thoroughly researched to focus on the unique requirements. Sensor-enabled IoT systems that deliver intelligent and smart services for smart agriculture are being investigated.
 Several case studies are offered to investigate the existing IoT-based solutions implemented by various organizations, individuals, and groups depending on their deployment criteria. The difficulties in these solutions are addressed, as are the factors for improvement and the future roadmap of work with IoT. Smart farming is a trend that emphasizes the use of information and communications technology (ICT) in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies such as the Internet of Things (IoT) and cloud computing are expected to spur growth and kickstart the usage of robotics and artificial intelligence in agriculture. Such radical departures are upsetting established agricultural practices while also posing several obstacles. This study explores the techniques and equipment utilized in wireless sensor applications in IoT agriculture, as well as the predicted problems encountered when integrating technology with traditional farming practices. Furthermore, this technical knowledge is useful to growers during crop times ranging from planting to harvest, and applications in packing and transportation are being researched.

Currently, the primary focus of every country worldwide is to enhance the efficiency of daily human life by embracing smart technology. Achieving a smart country status involves leveraging the internet, which enables various forms of smart work, predominantly through automation. Automation can be accomplished using Wireless Communication, Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (a subset of AI). Artificial Intelligence's role is constantly growing in human life and exerting a significant influence on society. In the context of Indian farming, Artificial Intelligence is progressively making its way and playing a vital role. In recent times, numerous countries have adopted machine learning and Artificial Intelligence in agriculture to predict and forecast crop yields, grade food, implement automated irrigation systems, and enhance the quality and quantity of crop production. This paper presents a comprehensive review of prior research conducted by various scholars, aiming to provide an overview of the applications and significance of Artificial Intelligence in agriculture, paving the way for automated and smart agricultural systems.

The internet of things (IoT) is focused in offering new technologies which is changing the business environment by showing the potential in different sectors like retail, transportation, healthcare etc. Through IoT, the managers can gather and process data and information in an effective manner, also they can use them to automate in order to enhance productivity and efficiency. With the usage of such technologies, human resource managers can access information for possible candidates which can be accessed through IoT. The overall behaviour of potential candidate tends to help them in shortlisting and choosing the desirable ones. The application of IoT has emerged in enhancing the recruitment of candidates in search of candidates based on large volume of information, assist in screening the candidates, selecting the most suitable ones. The application of IoT technology can modify the HR roles, enable in changing the company perspective in enhancing recruitment process, source the candidates effectively and select them so as to meet the organisational requirements. The implementation of IoT driven technologies support in removing the biases in screening the candidates and aids in candidate selection with the support of other technologies like artificial intelligence, predictive analytics etc. Furthermore, IoT is also involved in better inventory management as it supports the organisation in converting the data which are sourced through RFID (Radio Frequency Identification) into more insights related to inventory data, location of the products, tracking the movement of goods until it reaches the hands of the customers. With the application of IoT inventory managers possess better control in managing the inventory through automated ordering request and thereby reduce manual time and cost for the business. This paper is focused in presenting a conceptual understanding on the application of environmental IoT in recruitment process and also the overall impact created by the technology in managing the inventory efficiently.KeywordsInternet of thingsRecruitment processInventory managementPredictive analytics

[1]. Artificial intelligence (AI) in agriculture has the potential to revolutionize farming practices, improving productivity, sustainability, and decision-making. This study explores the application of AI in agriculture, focusing on how AI-driven tools and systems can optimize various farming processes, such as crop management, pest control, and yield prediction. The research evaluates the role of AI in addressing challenges faced by farmers and its impact on agricultural efficiency. The findings highlight the significant benefits of AI technologies in modernizing agriculture and creating sustainable farming solutions. Research Significance: The significance of this research lies in its ability to showcase the transformative potential of artificial intelligence in agriculture. AI applications can enhance precision farming, reduce resource wastage, and improve crop yields, all of which contribute to better sustainability and profitability for farmers. The study explores how AI technologies can address key challenges in agriculture, such as labor shortages, unpredictable weather patterns, and the need for optimized resource use. The findings offer valuable insights into how AI can drive agricultural innovation and help ensure food security in an increasingly volatile global environment. Methodology: COPRAS The COPRAS (Complex Proportional Assessment) methodology is utilized to assess and compare various AI-driven agricultural solutions and technologies. By evaluating multiple alternatives based on their effectiveness in improving agricultural practices, COPRAS provides a structured approach to selecting the most suitable AI solutions. The methodology takes into account factors such as cost-effectiveness, efficiency, and scalability, helping to identify the most impactful AI technologies for different agricultural needs. Alternative: AI-based crop monitoring systems, AI-driven irrigation technologies, AIpowered pest detection tools, AI-enhanced yield prediction models, AI-assisted supply chain management. Evaluation Parameters: Product price (C1), Company rating (C2), Delivery time (C3), Transportation costs (C4). Result: The results of the COPRAS analysis demonstrate that AI-driven technologies have the potential to significantly improve agricultural outcomes. AIpowered systems that offer efficient crop monitoring, precise irrigation, and early pest detection provide high value to farmers. Technologies with lower costs and faster deployment times, such as AI-based irrigation systems, tend to rank higher in the evaluation, making them more attractive to farmers seeking affordable solutions. However, the effectiveness and scalability of the solution are also crucial factors influencing the overall ranking

Revolutionizing agriculture: A comprehensive review on artificial intelligence applications in enhancing properties of agricultural produce.

Artificial Intelligence (AI) is emerging as a transformative force in the agricultural sector by improving productivity, optimizing resource utilization, and promoting sustainable farming practices. The integration of AI technologies such as machine learning, computer vision, robotics, Internet of Things (IoT), and big data analytics has enabled precision agriculture, smart irrigation systems, yield prediction models, pest and disease detection, and supply chain optimization. In countries like India, where agriculture remains a major source of livelihood, AI has the potential to address structural issues such as low productivity, climate variability, fragmented landholdings, and market inefficiencies. AI-powered platforms assist farmers in real-time decision-making by analyzing weather data, soil conditions, crop health, and market trends. Despite its transformative potential, the adoption of AI in agriculture faces several challenges including high initial costs, inadequate digital infrastructure, lack of technical awareness, data privacy concerns, and limited access to quality datasets. This research paper examines the role of AI in agriculture, evaluates its economic and social implications, and identifies the challenges associated with its implementation. The study concludes that effective policy support, public-private partnerships, capacity-building initiatives, and digital infrastructure development are crucial to maximizing the benefits of AI in agriculture.

Internet of Things (IoT) applications are generally developed in a vertical manner, i.e., each IoT application is developed for a certain scenario which generally does not share data with IoT applications for recommendation services. This leads to an inefficient exploitation of other IoT service applications. In fact, such recommendation services can be achieved with the help of Social IoT (SIoT) by using data generated by various IoT applications. SIoT builds a profile of objects based on IoT applications data that can be exchanged with SIoT network for accessible to other IoT applications. In this manner, the SIoT network provides recommendation services for reusability of IoT applications' data among various IoT applications, as well as adapting IoT services according to users' needs which improve user experience. Additionally, the profiles built by a SIoT network can also help a single IoT application by looking for similar conditions that have been addressed in the past for the same IoT application. We propose a concept for exploiting the SIoT for recommendation services among various IoT applications with the help of a sample application scenario that highlights how the SIoT can help by providing recommendations. We also provide some implementation challenges for this concept.

BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence

<p><span>This article investigates the transformative impact of low-cost internet of things (IoT) solutions on the agricultural sector, with a particular emphasis on integrating artificial intelligence (AI) and machine learning (ML) technologies. The study aims to illustrate how affordable IoT technologies, when combined with advanced AI and ML capabilities, can serve as a significant asset for small and medium-sized farms. It addresses the economic and technical barriers these farms face in adopting such technologies, including high initial costs and the complexity of implementation. By conducting a comprehensive evaluation of existing IoT hardware and software, the research identifies and highlights innovative, cost-effective solutions that have the potential to drive significant advancements in agricultural practices. The findings underscore how these integrated technologies can enhance operational efficiency, increase productivity, and support sustainable agricultural development. Additionally, the paper explores the potential challenges and limitations of adopting these technologies, offering insights into how they can be mitigated. Overall, the study demonstrates that the convergence of low-cost IoT with AI and ML presents a valuable opportunity for modernizing agriculture and improving farm management.</span></p>

The rapid digital development of the recent era has revolutionized the overall network of business and management as well as other sectors. The book discusses how emerging technologies, artificial intelligence (AI), blockchain, big data analytics, cloud computing and the Internet of Things (IoT), have a high level of impact on different industries. With more and more businesses turning towards these techs, it is extremely important to really understanding their relation and use in order to remain at the cutting edge of technology while emerging as an innovator. Chapter 1 is a detailed introduction to the digital transformation, driven by AI, blockchain, IoT and other technologies are changing Business & Management processes. This paves the way for diving deeper into targeted topics where these innovations are starting to have a big influence. In chapter two we delve into the increasing popularity of generative AI, ChatGPT takes center stage and how it is impacting range of industries. In this research, we review generative AI applications and opportunities as well as challenges and the outlook, for future development. Also touched on in the book are the customer front-exporting portions of digital renovation. Chapter 3 - Overall Vision: How AI, Machine Learning and related technologies help in higher customer satisfaction and loyalty right into the services industry to build exceptional service quality.

The integration of Artificial Intelligence (AI) in agriculture has emerged as a transformative approach to enhance productivity, sustainability, and resource efficiency. This study focuses on the optimum utilization of AI technologies such as machine learning, computer vision, and data analytics in modern farming practices. AI enables precise monitoring of crop health, soil conditions, weather patterns, and pest infestations, thereby supporting data-driven decision-making. Techniques like predictive analytics help in yield forecasting, irrigation management, and disease detection, reducing losses and operational costs. Furthermore, the use of AI-powered automation, including smart irrigation systems and autonomous machinery, minimizes human effort while maximizing output. The paper highlights the challenges in implementation, such as high initial investment, lack of technical awareness, and data accessibility issues, particularly in developing regions. It also explores potential solutions to overcome these barriers for widespread adoption. Overall, the optimum utilization of AI in agriculture can significantly contribute to food security, environmental conservation, and sustainable development.

The correct implementation and behavior of Internet of Things (IoT) applications are seldom investigated in the literature. This paper shows how the simulation mechanism can be integrated well into an IoT application development platform for correct implementation and behavior investigation. We use an IoT application development platform called IoTtalk as an example to describe how the simulation mechanism called SimTalk can be built into this IoT platform. We first elaborate on how to implement the simulator for an input IoT device (a sensor). Then we describe how an output IoT device (an actuator) can be simulated by an animated simulator. We use a smart farm application to show how the simulated sensors are used for correct implementation. We use applications including interactive art (skeleton art and water dance) and the pendulum physics experiment as examples to illustrate how IoT application behavior investigation can be achieved in SimTalk. As the main outcome of this paper, the SimTalk simulation codes can be directly reused for real IoT applications. Furthermore, SimTalk is integrated well with an IoT application verification tool in order to formally verify the IoT application configuration. Such features have not been found in any IoT simulators in the world.

As a major agricultural province in China, Shandong Province has always played an important role. At the same time, it is also closely related to the strong support of science and technology. As a new technology, the Internet of things (IoT) has made positive contributions. Agricultural IoT is a mode that uses information technology, wireless communication technology and radio frequency automatic identification technology to share the identified agricultural information to the network. The application of agricultural IoT is a major change in agricultural development, which provides convenient conditions for the development of agriculture, provides a favorable environment for the agricultural products, promotes the transformation of agricultural technology, realizes the informatization of agricultural production, improves the efficiency of rural land, integrates agricultural resources and provides a foundation for food safety. In this paper, the application of IoT in the field of agriculture in Shandong Province is described from two practical cases. One is about how to use wireless sensor network technology to transmit data in the big data platform based on the Bohai granary project. The other describes how to use IoT for food quality and safety traceability. It takes pigs as the research object, runs through all links of the whole industrial chain of pig breeding, breeding, slaughtering, processing, logistics and sales, collects the data of the whole industrial chain with the help of IoT, and realizes the information tracking and traceability of the whole industrial chain. At the end of the paper, it discusses the countermeasures.

Cloud computing, robotics, the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) are some of the technologies that make up the Internet of Robotic Things (IoRT). Transportation, industry, healthcare, and security all heavily rely on IoRT. IoRT has the potential to significantly accelerate human growth. Robots can send and receive data to and from other people and devices thanks to IoRT. This study reviews IoRT in terms of comparable architectures, methodologies, and capabilities. The associated research difficulties are so outlined. When creating robotic systems and objects, IoRT architectures are crucial. Human-robot cooperation, or HRC, is quickly emerging as a crucial element of smart manufacturing, healthcare, and service sectors due to the rapid integration of Internet of Things technology. However, real-world implementation of such systems poses challenging scheduling challenges due to the dynamic, diverse, and resource-constrained nature of IoT contexts. This paper presents a novel scheduling problem for real-world human–robot collaboration in Internet of Things applications, which aims to balance communication disruptions, computation demands, and safety-critical interactions. In contrast to traditional scheduling methods, which primarily deal with deterministic scenarios, the proposed perspective emphasizes flexibility in response to erratic human behavior, changing network conditions, and diverse device capabilities. The challenge is increased by the need to guarantee energy efficiency, real-time responsiveness, and secure data exchange across scattered IoT nodes. This scheduling problem needs to be fixed to improve system productivity and human safety, reduce job execution latency, and accomplish seamless coordination. This work lays the foundation for exploring new scheduling models that bridge the gap between theoretical frameworks and the practical requirements of IoT-enabled human–robot collaboration.

Under the background of prosperity of Internet of things (IoT), in order to accelerate the training of talented persons of China for IoT, we propose to design and develop a new course for Chinese students: Essential Technologies, Applications, and Commercial Patterns of Internet of Things. Aiming at three-dimensional target, which has dimensionalities of attitude and thoughts, knowledge and skills, process and methods, the contents of the course include the following parts: system and application architectures, key technologies of IoT, applications of IoT, commercial patterns of IoT, IoT industry and social significance etc. The course highlights the training of abilities of innovation, creativity, comprehensive analysis and practical operations. The construction of teaching resources, such as textbooks, lecture notes, teaching equipments, practice bases, are also emphasized in the process of development of this course.