Agricultural Internet Of Things Research Articles

Application of Internet-of-Things Wireless Communication Technology in Agricultural Irrigation Management: A Review

The integration of Internet-of-Things technology with traditional agricultural irrigation is a crucial factor in the advancement of traditional agricultural irrigation towards smart irrigation. Despite the widespread use of conventional irrigation methods in many areas, they lead to the significant wastage of both human and water resources. Therefore, the development of energy-saving and efficient intelligent irrigation systems through the application of Internet-of-Things technology and wireless communication technology is the way forward. This paper summarizes the common wireless communication technologies in the agricultural Internet of Things: Fifth-generation, WiFi, ZigBee, LoRa, and NB-IoT. The research status of the above wireless communication technology in agricultural irrigation management is discussed, and the agricultural irrigation management example using the above wireless communication technology is also presented. The advantages and limitations of the application of the above wireless communication technology in agricultural irrigation management are sorted out. Finally, this paper analyzes the challenges of data security issues, data fusion problems, intelligent irrigation system costs, power and energy problems, and system equipment failures faced by the use of IoT wireless communication technology in agricultural irrigation management. This review aims to assist researchers and users in choosing the most suitable wireless communication technology for diverse applications.

Smart Garden Management System Based on the combination of Internet of Things and Geographic Information System Technology

The Internet of Things (IoT) technology is inevitably forging ahead in a number of areas all among them being the agricultural sector and in particular the affected environment. This paper utilizes the farm's IoT where through an effective analysis, the smart garden is designed and managed. It blends field research, project implementations and theoretical analysis to improve the case implementation. It can be considered as more rigorous and practical. The study intends, first and foremost, to look at agricultural IoT based technologies and how IoT changes the way farmers work and agricultural landscapes look. Different theories have been used in the implementation of these concepts which include agriculture, tourism, and landscape design. The technical side will integrate the Internet of Things technology, the adaptation theory of agricultural industrialization, the formation of the theory of ecotourism, the application of tourism psychological theory in tourism discipline, the ecology theory of landscape and aesthetics theory, and landscape gardening planning and design theory. A comprehensive examination of the smart park has been conducted, including relevant theories and the application of planning and design principles. This systematic research aims to provide scientific direction for the agricultural aspects of the smart park. In order to offer scientific direction for the planning and design of agricultural Internet of Things (IoT) in smart gardens, this study presents a theoretical framework for the planning and design of smart gardens that incorporate agricultural IoT. The framework is comprehensively explained, encompassing its concept, distinguishing features, relevant theories, and guidance approaches.

Intelligent design and realization of sustainable development-oriented garden

The idea of sustainable development has become more important in resolving environmental issues and fostering a healthy coexistence of human endeavors with the natural world. Internet of Things (IoT) technology is expanding across many industries, and it is also advancing in agriculture and the agricultural environment. The planning and design for intelligent gardens using a unique Sunflower Optimized-Enhanced Support Vector Machine (SFO-ESVM) is thoroughly analyzed and researched in this study. The development and plan of intelligent gardens are investigated using agricultural IoT technologies and agricultural landscapes. First, we used the SFO method to select the best garden plan inspired by the mathematical patterns observed in sunflower seed groupings. Next, we use an ESVM model to assess how well each plant species fits into the planned garden. The SFO-ESVM considers several variables, such as soil qualities, climatic information, plant traits, and ecological requirements, to choose the best plants. Additionally, we create an intelligent control system that combines sensors, actuators, and IoT technologies to track and regulate the environmental parameters of the garden. The SFO-ESVM-based conceptual planning and design framework for smart gardens is proposed and systematically extended to give scientific direction for the agricultural IoT of smart gardens. The proposed method was then tested in a real-world garden environment. The outcomes show that the SFO-ESVM framework-based intelligent design and execution of the sustainable development-oriented garden combines ecological principles with innovative optimization methods.

Harnessing IoT for Smart Agriculture: A Comprehensive Exploration

The integration of Internet of Things (IoT) technologies in agriculture has revolutionized traditional farming practices, leading to the emergence of smart agriculture. This research paper aims to provide a comprehensive exploration of how IoT is harnessed in the field of smart agriculture. It discusses the various applications, benefits, challenges, and future prospects of IoT in agriculture. The study will delve into the various use cases of IoT in agriculture, such as monitoring sensors, data analysis, and automation. It will also examine the potential benefits of IoT in terms of increased productivity, sustainability, and cost savings. Additionally, the research will address the challenges associated with implementing IoT solutions in agriculture, including cost, data management, connectivity, and security. The findings of this study will provide valuable insights for farmers, agribusinesses, and researchers interested in leveraging IoT for smart agriculture.

A Self-Powered Biochemical Sensor for Intelligent Agriculture Enabled by Signal Enhanced Triboelectric Nanogenerator.

Precise agriculture based on intelligent agriculture plays a significant role in sustainable development. The agricultural Internet of Things (IoTs) is a crucial foundation for intelligent agriculture. However, the development of agricultural IoTs has led to exponential growth in various sensors, posing a major challenge in achieving long-term stable power supply for these distributed sensors. Introducing a self-powered active biochemical sensor can help, but current sensors have poor sensitivity and specificity making this application challenging. To overcome this limitation, a triboelectric nanogenerator (TENG)-based self-powered active urea sensor which demonstrates high sensitivity and specificity is developed. This device achieves signal enhancement by introducing a volume effect to enhance the utilization of charges through a novel dual-electrode structure, and improves the specificity of urea detection by utilizing an enzyme-catalyzed reaction. The device is successfully used to monitor the variation of urea concentration during crop growth with concentrations as low as 4µm, without being significantly affected by common fertilizers such as potassium chloride or ammonium dihydrogen phosphate. This is the first self-powered active biochemical sensor capable of highly specific and highly sensitive fertilizer detection, pointing toward a new direction for developing self-powered active biochemical sensor systems within sustainable development-oriented agricultural IoTs.

Co-Sharding: A Sharding Scheme for Large-Scale Internet of Things Application

Blockchain technology finds widespread application in the management of Internet of Things (IoT) devices. In response to the challenges posed by performance scalability and the convergence of multiple ledgers stemming from an expanding network, this study introduces the concept of Co-Sharding . Within this framework, the ledger maintained by sub-chains overseeing IoT operations in distinct geographic regions is conceptualized as a shard within the Large-scale Internet of Things (LIoT) ledger. Meanwhile, elected nodes within each region assume responsibility for maintaining a coordinating shard, facilitating cross-regional communication and data interaction. Furthermore, our work presents a multi-objective optimization algorithm grounded in the multi-shard paradigm to enact a scheduling strategy that spans various regions. We undertake a series of pertinent experiments and conduct a comparative analysis of scheduling algorithms within the context of a real-world cross-regional agricultural IoT system, utilizing actual operational data. The comparative results demonstrate that, in comparison to intra-sub-region scheduling, the Co-Sharding approach enhances machine utilization rates by approximately 30% and reduces scheduling time by around 18% when confronted with a task count of 12. In terms of performance, Co-Sharding also exhibits the capability to reduce the storage requirements of lightweight nodes within each region by approximately 39% while concurrently improving throughput by approximately 1.5 times when contrasted with a single-chain architecture.

LoRa Based IoT Enabled Sensor Networks for Plantations

The integration of Wireless Sensor Network (WSN) technology with the Internet of Things (IoT) in agriculture plays a pivotal role in addressing a range of challenges and constraints faced by the sector, encompassing issues such as labour shortages, suboptimal farm management practises, and unpredictable weather conditions. In response to these pressing concerns, this study focuses on the development of WSN with IoT for agriculture, employing a spread spectrum modulation technique named the Long Range (LoRa) module. By leveraging LoRa devices and wireless radio frequencies, this technology serves as a versatile platform for delivering wireless, long-range, and energy-efficient communication to support small and medium-sized agricultural operations. These operations often lack adequate technological assistance due to factors such as limited expertise and the high costs associated with cutting-edge agricultural technology. The research undertakes a comprehensive exploration, involving LoRa parameter testing, Line-of-Sight evaluations, Non-Line-Of-Sight (NLOS) simulations, and sensor calibration, to assess the efficacy of LoRa-based IoT-enabled sensor networks within plantation environments. The overarching objective of this research endeavour is to provide valuable insights that contribute to the optimisation of agricultural practises through streamlined IoT solutions. By implementing practical and cost-effective strategies, the local agricultural sector stands poised to achieve seamless strides in both sustainable and efficient food production.

Through the Soil Long Range Wireless Power Transfer for Agricultural IoT Networks

Increasing the spatial and temporal density of data using networked sensors, known as the Internet of Things (IoT), can lead to enhanced productivity and cost savings in a host of industries. Where applications involve large outdoor expanses, such as farming, oil and gas, or defense, large regions of unelectrified land could yield significant benefits if instrumented with a high density of IoT systems. The major limitation of expanding IoT networks in such applications stems from the challenge of delivering power to each sensing device. Batteries, generators, and renewable sources have predominately been used to address the challenge, but these solutions require constant maintenance or are sensitive to environmental factors. This work presents a novel approach where conduction currents through soil are utilized for the wireless powering of sensor networks, initial investigation is within an 0.8-ha (2-acre) area. The technique is not line-of-sight, powers all devices simultaneously through near-field mechanics, and has the ability to be minimally invasive to the working environment. A theory of operation is presented and the technique is experimentally demonstrated in an agricultural setting. Scaling and transfer parameters are discussed.

Retracted: Intelligent Garden Planning and Design Based on Agricultural Internet of Things

Retracted: Intelligent Garden Planning and Design Based on Agricultural Internet of Things

Harnessing IoT and machine learning for sustainable agriculture: Predictive crop yield modeling in smart farming

<p>The integration of Internet of Things (IoT) technology in agriculture has transformed traditional farming methods, enabling the emergence of smart agriculture systems. This research paper focuses on utilizing IoT and machine learning techniques to forecast crop yields based on climatic and soil conditions. The study utilizes a dataset sourced from Kaggle, which includes information on 22 distinct crops, such as Maize, Wheat, Mango, Watermelon, and others. The dataset encompasses crucial climatic factors like temperature, humidity, and rainfall, as well as essential soil conditions necessary for optimal crop growth. By employing advanced machine learning algorithms on this dataset, the objective is to develop accurate models capable of predicting crop yields. This, in turn, assists farmers in making well-informed decisions regarding crop management and optimizing agricultural productivity. The outcomes of this research have significant implications for the agricultural industry, providing valuable insights into crop yield estimation and supporting the implementation of sustainable farming practices.</p>

Harvesting the Future: AI and IoT in Agriculture

This review article, “Harvesting the Future: AI and IoT in Agriculture,” presents a comprehensive analysis of the transformative impact of Artificial Intelligence (AI) and the Internet of Things (IoT) in modern agriculture. It synthesizes a range of studies to showcase how AI and Machine Learning (ML) algorithms are revolutionizing crop management, precision agriculture, and supply chain efficiency. Utilizing data from various sources like sensors, drones, and satellites, these technologies enable optimized resource use, enhanced crop yields, and better livestock health monitoring. The review highlights the role of IoT in agriculture, particularly its benefits in easy installation, reduced maintenance, and energy harvesting for device sustainability. It explores the integration of IoT with big data and knowledge-based systems, addressing key challenges in farm data management. Additionally, the paper delves into the growing adoption of smart agriculture techniques, driven by cost-effective IoT sensors and AI advancements. These technologies facilitate efficient resource management, predictive analytics, and autonomous farming, thereby modernizing traditional agricultural practices. The review also discusses the broader social implications and future trends in the adoption of smart agriculture systems, describing their significance in enhancing agricultural productivity, sustainability, and profitability.

Real-time Monitoring and Early Warning of Cotton Diseases and Pests Based on Agricultural Internet of Things

During the entire process of cotton growth and development, diseases and pests occur frequently. Therefore, effective prevention and control of them has always been a difficult problem for agricultural scientists and cotton farmers. This article designed a real-time monitoring and early warning system for cotton pests and diseases. The data processing and analysis module can process and analyze the collected data, and uses technologies such as image recognition and data mining to achieve real-time recognition of cotton diseases and pests. Real-time monitoring of its operation was achieved through the mobile display and management module, enabling users to timely grasp the situation of cotton fields and take corresponding measures. This article compared the monitoring effectiveness and efficiency of traditional manual monitoring methods and agricultural Internet of Things (IoT) based systems in the same farmland. The accuracy of traditional manual monitoring methods was 85%, while agricultural IoT systems showed higher accuracy, reaching 95%. Real-time monitoring and early warning of cotton diseases and pests based on the agricultural Internet of Things is helpful for the prevention and control of flower diseases and pests and cotton field management.

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use blockchains are either highly complex or require large delays & have higher energy consumption for larger networks. Moreover, the efficiency of these models depends directly on consensus-efficiency & miner-efficiency, which restricts their scalability under real-time scenarios. To overcome these limitations, this study proposes the design of an efficient Q-Learning bioinspired model for enhancing QoS of AIoT deployments via customized shards. The model initially collects temporal information about the deployed AIoT Nodes, and continuously updates individual recurring trust metrics. These trust metrics are used by a Q-Learning process for identification of miners that can participate in the block-addition process. The blocks are added via a novel Proof-of-Performance (PoP) based consensus model, which uses a dynamic consensus function that is based on temporal performance of miner nodes. The PoP consensus is facilitated via customized shards, wherein each shard is deployed based on its context of deployment, that decides the shard-length, hashing model used for the shard, and encryption technique used by these shards. This is facilitated by a Mayfly Optimization (MO) Model that uses PoP scores for selecting shard configurations. These shards are further segregated into smaller shards via a Bacterial Foraging Optimization (BFO) Model, which assists in identification of optimal shard length for underlying deployment contexts. Due to these optimizations, the model is able to improve the speed of mining by 4.5%, while reducing energy needed for mining by 10.4%, improving the throughput during AIoT communications by 8.3%, and improving the packet delivery consistency by 2.5% when compared with existing blockchain-based AIoT deployment models under similar scenarios. This performance was observed to be consistent even under large-scale attacks.

Revolutionizing Agriculture: The Impact of AI and IoT

This review comprehensively examines the role of Artificial Intelligence (AI) and the Internet of Things (IoT) in revolutionizing agricultural practices. It highlights how these technologies are pivotal in enhancing crop productivity, optimizing resource use, and ensuring sustainability in response to the challenges of a growing global population and environmental concerns. The article synthesizes key studies to showcase advancements in smart aquaponics and marine farming, emphasizing the the importance of implementation of AI and IoT in agriculture faces significant challenges, including high costs, a digital skills gap among farmers, data management issues, and the need for robust technology. The review also addresses the socio-economic impacts, such as potential job displacement due to automation. Looking ahead, the article suggests that overcoming these challenges will require concerted efforts in technological innovation, policy-making, and education. The future of AI and IoT in agriculture is seen as promising, with potential for further advancements and wider adoption. The integration of these technologies with emerging fields like blockchain could lead to more secure, efficient, and transparent agricultural practices, contributing significantly to global food security and environmental sustainability.

From Field to Fork: The Role of AI and IoT in Agriculture

In "From Field to Fork: The Role of AI and IoT in Agriculture," we explore the transformative impact of cutting-edge technology in the farming industry and its journey through the food supply chain. This article delves into how the Internet of Things (IoT) and Artificial Intelligence (AI) are revolutionizing the way food is grown, processed, and brought to market. It looks at key technologies such as Radio Frequency Identification (RFID), sensor networks, and Cloud Computing, highlighting their role in enhancing food production, streamlining transportation, and improving distribution methods. The review sheds light on the growing importance of big data, gathered from sensors in the fields and online social media platforms, in refining agricultural methods, from monitoring crops using drones to bringing supply chains into the modern age. The paper also examines the vital function of AI in fine-tuning farming practices, safeguarding food quality, and ensuring food safety. Additionally, it addresses the need for secure data management in agricultural production and discusses how blockchain technology can provide reliable traceability from the field to consumers' tables. Wrapping up with an overview of current trends and future prospects, the article emphasizes the crucial role of AI and IoT not just in enhancing agricultural efficiency and minimizing waste, but also in their potential to meet Keywords: Fork, AI and IoT, Agriculture, food.

An Efficient Agricultural Data Analysis Based On IOT

Science-based agriculture is increasing due to the rapid development of science and technology. To keep up with today's technology, many agricultural experts are turning to the Internet of Things (IoT) to drive advances in this field. For India, agriculture is the backbone of the Indian economy. The most important issue in today's technology is climate change. This review aims to provide an overview of IoT in agriculture and food to better understand the use of IoT and how it solves the above problems. Start by defining and further explaining the concept of IoT. The next section summarises the analysis results and discusses application areas, technical support, and future challenges.

Precision Farming: The Power of AI and IoT Technologies

This review article titled "Precision Farming: The Power of AI and IoT Technologies" delves into the transformative impact of Artificial Intelligence (AI) and the Internet of Things (IoT) in revolutionising modern agricultural practices. This paper explores the integration of cutting-edge technologies, including AI, IoT, remote sensing, and Geographic Information Systems (GIS), in addressing the challenges posed by diminishing agricultural resources and an expanding population. Precision farming, empowered by these technologies, offers a paradigm shift from traditional farming methods to a more data-driven, efficient, and environmentally sustainable approach. The use of AI and IoT in agriculture has led to significant advancements in crop monitoring, resource management, and decision-making processes. However, the implementation of these technologies is not without challenges, such as accessibility, connectivity, and the complexity of integration. This article reviews the current state and future prospects of precision farming, emphasising its role in enhancing agricultural productivity while minimising environmental impact. The potential for future innovations in this field is vast, promising a new era of efficient and sustainable farming practices essential for global food security.

IoT enabled smart agriculture

Individuals in India get by off agribusiness. Significant job in nation's economy. Relocation from provincial to metropolitan regions impedes farming today. Observing the climate alone won't support farming efficiency. No significant factors lessen efficiency. To settle these issues, agribusiness needs robotization. An independent water system framework saves ranchers time, cash, and power. Customary homestead water system requires actual inclusion. With programmed water system, human association is diminished. Sensors and the Internet of Things (IOT) can persistently screen crops and illuminate ranchers about crop development and gather time, expanding crop efficiency and guaranteeing ideal item conveyance to purchasers. We use IOT for brilliant agriculture to tackle this issue. This venture gathers and cycles field information from temperature, humidity, soil moisture, and rain sensors. These sensors use web innovations to remotely control and screen sensor information by means of a wireless sensor network.

Performance Analysis of Distributed Database Management System for IoT in the Context of Smart Agriculture in Indonesia

The integration of Distributed Database Management Systems (DDBMS) for Internet of Things (IoT) applications in smart agriculture presents a transformative paradigm in the dynamic landscape of Indonesian agriculture. This qualitative research investigates the challenges and opportunities associated with this integration, drawing insights from interviews with diverse stakeholders, including farmers, technology developers, and policymakers. The technical challenges encompass data synchronization, network latency, and security considerations, emphasizing the need for standardized protocols and robust security measures. Infrastructure challenges, such as connectivity issues and power supply dependency, underscore the importance of rural infrastructure development. User acceptance challenges highlight resistance to change and the perceived complexity of DDBMS, emphasizing the significance of targeted awareness and training programs. Despite these challenges, stakeholders recognize the advantages of scalability, enhanced data availability, and improved decision-making capabilities offered by DDBMS. The study proposes recommendations, including technological solutions, policy interventions, and capacity-building initiatives, to address identified challenges and promote the effective utilization of DDBMS in Indonesian smart agriculture. This research contributes context-specific insights to the global discourse on IoT and DDBMS in agriculture.

A Platform for GHG Emissions Management in Mixed Farms

This research introduces an innovative platform designed to manage greenhouse gas (GHG) emissions in mixed farms. Emphasizing the urgent need to address GHG emissions, particularly methane (CH4) and nitrous oxide (N2O), the platform targets mixed farming systems where the interplay of livestock and crop production significantly contributes to environmental impacts. Our methodology is grounded in comprehensive data collection, encompassing soil data, energy consumption, and detailed livestock information. Utilizing the Agricultural Internet of Things (AIoT), it facilitates real-time data acquisition and analysis, providing insights into various farm activities’ GHG emissions. This approach allows for precise monitoring and management of emissions from different sources, including enteric fermentation in livestock and fertilizer use in crop production. Results from the application show its effectiveness in offering a clear and interactive visualization of GHG emissions, aiding farmers in making informed decisions for sustainable farm management. The platform’s user management system, coupled with advanced data processing and visualization capabilities, underscores its potential as a vital tool for sustainable farming. Conclusively, Solution4Farming represents a significant advancement in digital agriculture, combining IoT technology with sustainable practices. Though initially designed for Romanian cattle farming, Solution4Farming’s anticipated expansion to various farming environments suggests a broader impact and relevance in sustainable agriculture.