Agricultural Internet Research Articles

RETRACTION: Agricultural Internet of Things Application in the Construction of Regional Smart Cities

RETRACTION: Agricultural Internet of Things Application in the Construction of Regional Smart Cities

Reliability Evaluation Framework for Centralized Agricultural Internet of Things (Agri-IoT)

Reliability Evaluation Framework for Centralized Agricultural Internet of Things (Agri-IoT)

Research on the application of water measuring technology in water-saving irrigation

Abstract The rise of agricultural Internet of Things (IoT) technologies is transforming traditional farming into modern, sustainable agriculture through scientific advancements. This study introduces a novel fusion calibration algorithm that combines the least squares method and back propagation neural networks to enhance water measurement accuracy in agricultural settings. By developing a nonlinear function, the algorithm progressively minimizes the discrepancies between detected results and actual data until satisfactory accuracy is achieved. An irrigation experiment on wolfberry plants in City H utilizing this optimized technology demonstrated a near-perfect correlation between the automatic measurements (3.56 m/s) and the actual flow rates (3.55 m/s) recorded by a flow meter, with an error margin of just 0.282%. Furthermore, the study observed a steady increase in the water utilization coefficient in farmland irrigation from 0.54 in 2011 to 0.589 in 2020, indicating enhanced water efficiency and conservation.

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.

Developing a Dynamic Marketing Channel Model for Agricultural Cooperatives in the New Media Economy Using the Power of the Internet of Things

Abstract The marketing channel is the lifeline of agricultural product circulation and marketing. Multi-level and multi-channel smooth circulation of agricultural products is an important way for agricultural cooperatives to realize the value of their goal. This paper builds an agricultural Internet of Things service system to lay an informatization foundation for the development of dynamic marketing channels of “new media platform + agricultural cooperatives”. With the use of new media platforms as an operational mode and the marketing strategy of agricultural cooperatives as guidance, the marketing effectiveness of agricultural products can be improved. Adopting Internet thinking and innovation trends, comprehending the characteristics of new media marketing, and encouraging collaboration between new media platforms and agricultural cooperatives. Based on 28,450 agricultural cooperatives in China, the marketing channel mode selection and characteristic data were analyzed, and it was found that among the 2,617 cooperatives using new media platforms for channel marketing, the product price fluctuation was between 0 and 10%, and the difficulty of product sales was stable in the interval of [0,40], which indicated that the new media marketing channel could make the price of agricultural products stable and easier to sell. Evolutionary game analysis found that the choice of cooperation or non-cooperation between agricultural cooperatives and new media platforms is related to the coefficient of benefit analysis, the coefficient of cooperation cost, and the liquidated damages of the cooperation between the two parties. Therefore, in order to improve the intensity of cooperation between new media platforms and agricultural cooperatives, improvements should be made in terms of benefits, costs, and liquidated damages.

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.

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.

Retracted: Crop Growth Monitoring System Based on Agricultural Internet of Things Technology

Retracted: Crop Growth Monitoring System Based on Agricultural Internet of Things Technology

Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture

Agriculture is considered one of the most critical sectors that play a strategic role in ensuring food security. It is directly related to human development and social stability. The agricultural sector is currently incorporating new technologies from other areas. These phenomena are smart agriculture and smart irrigation. However, a challenge to research is the integration of technologies from different knowledge fields, which has caused theoretical and practical difficulties. Thus, our purpose in this study has been to understand the core of these two themes. We extracted publications in Scopus and used bibliometric methods for high-frequency word and phrase analysis. Research shows that current research on smart agriculture mainly focuses on the Internet of Things, climate change, machine learning, precision agriculture and wireless sensor networks. Simultaneously, the Internet of Things, irrigation systems, soil moisture, wireless sensor networks and climate change have received the most scholarly attention in smart irrigation. This study used cluster analysis to find that the IoT has the most apparent growth rate in smart agriculture and smart irrigation, with five-year growth rates of 1617% and 2285%, respectively. In addition, machine learning, deep learning and neural networks have enormous potential in smart irrigation compared with smart agriculture.

Revolutionizing Energy Harvesting: Solar Cell Integration with Radio Waves for AI, IT, and Business Applications

The dynamic convergence of solar cell technologies, radio waves, information technology (IT), artificial intelligence (AI), and business applications is examined in this study. The combination of these technological components has created a diverse array of innovative opportunities that provide cost-effective, environmentally friendly, and game-changing solutions for a range of businesses. We start by exploring the basic ideas behind solar cells, demonstrating their ability to transform light from the sun into electrical energy, and looking at the various kinds of solar cells and how they work together to gather energy. With uses ranging from off-grid, rural locations to space exploration and residential and commercial installations, solar cells have emerged as key players in the shift to clean, renewable energy sources. Next, we delve into the world of radio waves, which are essential to radar systems, wireless communication, remote sensing, and other fields. We clarify the various uses of radio waves and the difficulties they pose, paving the way for their cooperation with solar cell technology. The combination of radio waves with solar cells opens up new opportunities for business, IT, and artificial intelligence applications. With this connection, off-grid communication networks may be made more sustainable and wireless devices and sensors can be installed in remote or energy-constrained areas. There could be a huge impact on AI-driven applications such as Internet of Things (IoT), smart cities, precision agriculture, and remote data collection. Integration of solar-powered radio wave technology improves sustainability, resilience, and connectedness in the field of information technology. It enables companies to improve supply chain operations, lower energy costs, operate profitably in isolated or disaster-prone places, and support sustainable practices. Among the noteworthy trends influencing the IT sector are edge computing and digital inclusion. We also examine the significant effects of solar-powered radio wave technologies on the business sector. It increases supply chain optimization, energy efficiency, environmental monitoring, and remote monitoring and control. Key themes in this environment include resilience, scalability, and sustainability. Businesses are looking for creative ways to adapt to changing customer demands and legal obligations.

Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review

The potential benefits of applying information and communication technology (ICT) in precision agriculture to enhance sustainable agricultural growth were discussed in this review article. The current technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), as well as their applications, must be integrated into the agricultural sector to ensure long-term agricultural productivity. These technologies have the potential to improve global food security by reducing crop output gaps, decreasing food waste, and minimizing resource use inefficiencies. The importance of collecting and analyzing big data from multiple sources, particularly in situ and on-the-go sensors, is also highlighted as an important component of achieving predictive decision making capabilities in precision agriculture and forecasting yields using advanced yield prediction models developed through machine learning. Finally, we cover the replacement of wired-based, complicated systems in infield monitoring with wireless sensor networks (WSN), particularly in the agricultural sector, and emphasize the necessity of knowing the radio frequency (RF) contributing aspects that influence signal intensity, interference, system model, bandwidth, and transmission range when creating a successful Agricultural Internet of Thing Ag-IoT system. The relevance of communication protocols and interfaces for presenting agricultural data acquired from sensors in various formats is also emphasized in the paper, as is the function of 4G, 3G, and 5G technologies in IoT-based smart farming. Overall, these research sheds light on the significance of wireless sensor networks and big data in the future of precision crop production

AN AGRICULTURAL INTERNET OF THINGS (A-IOT) BASED INTELLIGENT SYSTEM FOR DISEASE PREDICTION USING TRANSFER LEARNING, A CASE STUDY

The agricultural Internet of Things (IoT) has altered agricultural output in unprecedented ways. In addition to raising agricultural productivity, it may also significantly raise product quality, lower labor costs, boost farmer income, and achieve agricultural modernization and intelligence. To predict diseases in the agricultural sector, this article proposed an IoT-based smart system for agriculture. The current state of agricultural IoT is first illustrated, along with a summary of its system architecture. In order to predict diseases in the agricultural domain and advance agricultural IoT, an intelligent system based on agriculture is being proposed in this study. The MATLAB 2020a tool is used for simulation and results. In the proposed industrial IoT based intelligent system, a transfer learning model is applied for the training and validation of rice leaf disease prediction in agriculture industry 4.0. The result of the proposed industrial IoT based intelligent system achieved 96.95% accuracy, which is better than the state-of-the-art published methods.

Image Processing Algorithm Design of Agricultural Internet of Things Platform Based on Big Data Analysis

Abstract To solve the problems of low signal to noise ratio (SNR), poor processing effect, and long processing times for traditional image processing algorithms, an image processing algorithm for the agricultural Internet of Things platform based on big data analysis was designed. The big data analysis method was used for agricultural low-quality image recognition, and the Internet of Things platform uses sparse representation and a complete dictionary of low-quality image denoising processing with an improved histogram equalization method to enhance the image denoising result and get a high-quality image. Through this, the image processing algorithm design of the agricultural Internet of Things platform was completed. Experimental results show that the algorithm has high SNR and a good image processing effect, and the image processing time is always below 0.7 s, which meets research expectations.

Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet

Integrating cutting-edge technology with conventional farming practices has been dubbed “smart agriculture” or “the agricultural internet of things.” Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks.

An All-Printed, Fast-Response Flexible Humidity Sensor Based on Hexagonal-WO3 Nanowires for Multifunctional Applications.

The utilization of printing techniques for the development of high-performance humidity sensors holds immense significance for various applications in the fields of the Internet of Things, agriculture, human healthcare, and storage environments. However, the long response time and low sensitivity of current printed humidity sensors limit their practical applications. Herein, a series of high-sensing-performance flexible resistive-type humidity sensors is fabricated by the screen-printing method, and hexagonal tungsten oxide (h-WO3 ) is employed as the humidity-sensing material due to its low cost, strong chemical adsorption ability, and excellent humidity-sensing ability. The as-prepared printed sensors exhibit high sensitivity, good repeatability, outstanding flexibility, low hysteresis, and fast response (1.5s) in a wide relative humidity (RH) range (11-95%RH). Furthermore, the sensitivity of humidity sensors can be easily adjusted by altering the manufacturing parameters of the sensing layer and interdigital electrode to meet the diverse requirements of specific applications. The printed flexible humidity sensors possess immense potential in various applications, including wearable devices, non-contact measurements, and packaging opening state monitoring.

A Lightweight, Secure Authentication Model for the Smart Agricultural Internet of Things

The advancement of smart agriculture, with information technology serving as a pivotal enabling factor, plays a crucial role in achieving food security, optimizing production efficiency, and preserving the environment. Simultaneously, wireless communication technology holds a critical function within the context of applying the Internet of Things in agriculture. In this research endeavor, we present an algorithm for lightweight channel authentication based on frequency-domain feature extraction. This algorithm aims to distinguish between authentic transmitters and unauthorized ones in the wireless communication context of a representative agricultural setting. To accomplish this, we compiled a dataset comprising legitimate and illegitimate communication channels observed in both indoor and outdoor scenarios, which are typical in the context of smart agriculture. Leveraging its exceptional perceptual capabilities and advantages in parallel computing, the Transformer has injected fresh vitality into the realm of signal processing. Consequently, we opted for the lightweight MobileViT as our foundational model and designed a frequency-domain feature extraction module to augment MobileViT’s capabilities in signal processing. During the validation phase, we conducted a side-by-side comparison with currently outstanding ViT models in terms of convergence speed, precision, and performance parameters. Our model emerged as the frontrunner across all aspects, with FDFE-MobileViT achieving precision, recall, and F-score rates of 96.6%, 95.6%, and 96.1%, respectively. Additionally, the model maintains a compact size of 4.04 MB. Through comprehensive experiments, our proposed method was rigorously verified as a lighter, more efficient, and more accurate solution.

Internet of agriculture: Analyzing and predicting tractor ride comfort through supervised machine learning

The aim of this study is to improve ride comfort among tractor drivers by utilizing Internet of Things (IoT) technology and Machine Learning (ML) techniques to analyze and predict vibration exposure. The study explores the association between tractor ride characteristics, including average speed, tool depth, and pulling force, and ride comfort during actual rotary soil tillage operations. Ride comfort was evaluated by calculating the Overall Vibration Value (OVV) at three measurement locations (i.e., the floor, seat pan, and seat backrest). The study employed spectral analysis i.e., Fast Fourier Transform (FFT) and Power Spectral Density (PSD) to determine the predominant resonant frequencies. In addition, ML approaches i.e., Linear Regression (LR), Decision Tree Regressor (DTR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) were used to predict ride comfort response. Three hyperparameter optimization techniques, including Grid Search, Random Search, and Bayesian optimization were used. Tractor-tiller system was operated on a real agricultural field by five male tractor drivers. Vibration levels were measured along three translational axes at three measurement locations using a smart monitoring device based on the IoT concept. Average OVV magnitude was computed as 0.84 m/s2 i.e., exceeding the ISO 2631-1 (1997) threshold limits. FFT and PSD analysis revealed a variety of dominant frequencies, including resonance peaks at 2 Hz, 6–7 Hz, and 9–11 Hz. The analysis of variance (ANOVA) indicates that tractor speed and tool depth are significant variables (at the 5% level) in determining ride comfort. Regarding predictive accuracy, the ANN model exhibits the highest coefficient of determination of 0.90, followed by the SVM and GPR models with a coefficient of determination of 0.89. The DTR and LR models demonstrate slightly lower coefficient of determination values of 0.83 and 0.82, respectively. Furthermore, Bayesian optimization proved to be an effective approach in achieving more accurate predictions of tractor ride comfort. In general, the study outcomes can be utilized by tractor designers in numerous ways to optimize tractor design (such as suspension system, cab design, seat design, etc.) to improve tractor ride comfort in real field applications.

Evaluation of the willingness and behavior of family farm water resource financing based on the Internet of Things SEM model

Abstract At present, China's rural water resources are in short supply and the water pollution situation is severe. Family farms are an important part of China's agricultural modernization, and their development level is an important indicator to measure the degree of modernization of a country and a region. The application of agricultural Internet of Things technology in the field of agriculture is helpful for solving the problem of water shortage in family farms in water shortage areas. Based on questionnaire data, this paper used structural equation modeling (SEM) to study the relationship between family farm water financing willingness and behavior. The results showed that the standardization coefficients of Assumption 1, Assumption 2 and Assumption 3 were 0.332, 0.267 and 0.311, respectively. It can be seen that the water resource financing willingness of family farms was greatly affected by their water-saving technology ability, water management ability and government policy support. However, the standardization coefficient of Assumption 5 was 0.087. It can be seen that the water management capacity had no significant impact on the water resource financing behavior, and the water resource financing behavior of family farms was mainly affected by their water-saving technical capacity and government policy support.

Thinking about the strategy and practice path of modern agricultural industry development in the context of big data

Abstract China is in the critical period of “four synchronous development” of industrialization, informatization, urbanization, and agricultural modernization, so it is urgent to find a correct, scientific, and reasonable development strategy for modern agricultural products and promote the development of big data agriculture. In this paper, we use big data technology to determine the algorithm model of agricultural big data technology and the application system of the agricultural Internet of Things and argue for big data for agricultural planting technology, agricultural economic management, and agricultural industry upgrading in order to find the optimal strategy for the development of modern agriculture in China. As of the statistics at the end of 2019, China’s arable land transfer area has reached 440 million mu, accounting for 30.8% of the total contracted arable land area. With the subsequent land transfer entering a standardized and normalized stage, the scale operation of agricultural production is bound to speed up. In recent years, due to the application of big data technology in agricultural production, China’s modern agricultural industry has developed rapidly, with the mechanization rate of farming at around 86% and the contribution rate of science and technology at over 67%, and agricultural production has gained breakthroughs nationwide. Thus, it can be seen that the modern agricultural industry in the context of big data will usher in new development opportunities.

Survey of Intelligent Agricultural IoT Based on 5G

In the future, agriculture will face the need for increasing production, sustainability, wisdom, and efficiency, which will bring significant challenges to the development of modern agriculture. With the gradual popularization of 5G, advanced information technologies such as the Internet of Things and artificial intelligence promoted the evolution of modern agriculture to intelligent agriculture. The 5G-based Internet of Things will play an essential role in the development of smart agriculture. This paper investigates the research progress of 5G Internet of Things in smart agriculture. It sorts out the development status of 5G smart agriculture Internet of Things in recent years. Following that, the concept of 5G smart agriculture Internet of Things is put forward. It expounds on the connotation, architecture, and enabling key technologies. According to the key application scenarios of smart agriculture, practical cases are presented, the development trend and application value of 5G smart agriculture Internet of Things are shown, and the future development direction is put forward. Firstly, the concept of smart agriculture is distinguished, and the category scenarios of smart agriculture are summarized. Following that, the current review research on 5G-IoT is analyzed. This paper focuses on the analysis and summary of the changes brought by 5G to various key scenarios in smart agriculture. This paper analyzes the related key technologies and challenges, puts forward some key scientific problems, and summarizes the research ideas. Finally, the development trend and application value of 5G smart agriculture Internet of Things are shown. The future development direction is also proposed.