Agricultural Internet Research Articles

Risk monitoring model of intelligent agriculture Internet of Things based on big data

With the development of the times, there is a huge amount of data in every industry. Big data technology is to collect, analyze, process and information from these huge data and apply it to all aspects of our life to improve people's production life. The proposal and development of smart agriculture will play a significant role in the further implementation of the country's rural revitalization strategy. However, the research on risk monitoring in smart agriculture is not yet systematic enough. The purpose of this article is to strengthen the development of smart agriculture under big data, focusing on risk monitoring. To this end, this article studies big data monitoring through data analysis methods and Internet of Things technologies, and discusses the principles of big data and key technology principles of the Internet of Things. Explained, and proposed a modern agricultural technology platform based on the Internet of Things and big data. This platform is established on the basis of precision agriculture and wireless sensor network work, and analyzes the accuracy of various types of light wavelengths for determining wheat rust. The analysis results show that the accuracy of light with multiple wavelengths is not necessarily better than the light with a single wavelength. The accuracy of 600 nm wavelength can reach 100 %, and the accuracy of monitoring with 4 wavelengths together is rather low. However, less consideration is given to the factors in this article, and wheat does not necessarily cause only one disease. Comprehensive analysis is required. With the help of spectral research and aerial photography of drones, the severity of the disease and the outbreak area can be speculated.

Construction on Evaluation Index System of Traceability System of Agricultural Internet of Things Based on Network Analysis

In order to ensure the transparency and openness of each link of agricultural products “from farmland to table” and to solve the problem of consumers’ low trust degree in the safety of agricultural products and food, the application and evaluation of agricultural Internet of Things traceability system are the top priority. However, the traceability system evaluation indicators are rarely discussed in the existing studies. Consequently, this paper attempts to construct the evaluation index system of the Internet of Things traceability system of agricultural products based on an overall perspective of ecological civilization and to determine the weight of each evaluation index by using analytic hierarchy process (AHP). Then, on the basis of the relevant data of the agricultural product traceability system in recent years, the determined weight is modified by using entropy method. Finally, it points out the important practical value for management departments, production subjects, sales subjects, and consumers, providing reference for improving the quality and safety of agricultural products and standardizing the market supervision of agricultural product market.

Climate‐smart agriculture using intelligent techniques, blockchain and Internet of Things: Concepts, challenges, and opportunities

AbstractThe Internet of Things (IoT) is an important technology that provides efficient and dependable solutions in a variety of domains, such as smart agriculture and climatic change. It integrates billions of smart devices that can communicate with one another and gives solutions to automatically maintain and monitor smart agricultural and environmental fields. The combination of IoT, Artificial Intelligence (AI), and blockchain technology will allow us to transform smart agriculture into the Internet of smart agriculture, providing greater control, management, and security in supply‐chain networks. This paper presents an overview of the technologies in the domains of IoT, Climate‐Smart Agriculture (CSA), AI, Machine Learning (ML), and blockchain. In addition, the paper presents several approaches for integrating IoT with CSA data analysis. Both AI and blockchain are adopted for efficient CSA systems. The paper is concerned with the combination of three recent technologies: IoT, ML, and blockchain to serve the CSA applications. The challenges and opportunities of combining these technologies to serve CSA are also discussed in the paper.

WIFI BASED SMART AGRICULTURE

Smart agriculture is an emerging concept, because IOT sensors are capable of providing information about agriculture fields and then act upon based on the user input. The goal of this paper is to create a Smart Agriculture System that takes advantage of cutting-edge technologies like Arduino, IoT, and Wireless Sensor Network. The goal of the article is to use developing technologies, such as the Internet of Things (IoT) and smart agriculture with automation. The ability to monitor environmental variables is a key aspect in increasing the production of efficient crops. The invention of a system that can monitor temperature, humidity, wetness, and even disease detection in tomato leaves is a characteristic of this research. TCP/IP is used in the development of the application for the farmer's Smartphone. The system uses a duplex communication link based on a cellular-Internet interface, allowing data inspection and irrigation scheduling to be configured using an android application. The device has the potential to be beneficial in water-scarce, geographically isolated places due to its energy independence and low cost.

Rethinking Sustainable Sensing in Agricultural Internet of Things: From Power Supply Perspective

Agricultural Internet of Things (IoT) is expected to address several challenges facing the current agriculture industry, including food production, food safety, ecological environment protection, and food waste. However, before achieving this blueprint, a fundamental problem that should be addressed is the sustainability. Unfortunately, solar energy harvesting, which is today's common approach for sustainable agricultural IoT, has many limitations and can barely support future development. This article bridges this gap by proposing a versatile power supply paradigm, called PowerEdge, that organically integrates ambient energy harvesting, distributed energy storage, wireless power transfer, and intelligent reflecting surface techniques to achieve sustainable smart agricultural operations. A proof of concept with commercially available products is also presented, along with extensive experimental studies in five scenarios. Several interesting novel observations are found. Finally, four technical challenges and four open research issues associated with the proposed solution are discussed.

Application of Big Data Analysis to Agricultural Production, Agricultural Product Marketing, and Influencing Factors in Intelligent Agriculture

Agricultural Internet of things (AIoT) promotes the modernization of traditional agricultural production and marketing model. However, the existing time series prediction methods for agricultural production and agricultural product (AP) marketing cannot adapt well to most real-world scenarios, failing to realize multistep forecast of production and AP marketing data. To solve the problem, this paper explores the big data analysis of agricultural production, AP marketing, and influencing factors in intelligent agriculture. To realize long-, and short-term predictions, a small-sample time series model was set up for AIoT production, and a big-sample time series model was constructed for AP marketing. The data fusion algorithm based on Kalman filter (KF) was adopted to fuse the massive multi-source AP marketing data. The proposed strategy was proved valid through experiments.

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

Smart city originated from the field of media, which refers to the use of various information technologies or innovative concepts to connect and integrate urban systems and services. It improves the efficiency of resource utilization, optimizes urban management and services, and improves the quality of life of citizens. A smart city is a new generation of information integration technologies using the internet of things, cloud computing, big data, and spatial geographic information integration. It is a new theory and new model to promote urban planning, construction, management, and service intelligence. The IoT technology originated from the third technological revolution. It refers to the connection of objects to the network through the information sensor equipment according to the agreed protocol. It communicates and circulates information through the information dissemination medium during the connection process to realize intelligent identification, tracking, supervision, and other functions. The internet of things is the internet where everything is connected. It is an extension and expansion of the internet based on the network. It is a huge network formed by combining various information sensing devices with the internet. In fact, people, machines, and things are interconnected at any time and at any place. Agricultural internet of things refers to the internet of things that participates in automatic control through various instruments and equipment as parameters of automatic control. Its essence is to use RFID, wireless data communication, and other technologies to construct an “internet of things” covering everything in the world on the basis of the computer internet. It realizes the automatic identification of items and the interconnection and sharing of information through the computer internet. This study aims to study the application of the agricultural internet of things in the construction of regional smart cities. It expects to promote the development of modern agriculture by improving the level of agricultural management with the help of smart city and internet of things technology. Based on the B/S system model, this study uses the related technologies of the internet of things to build a smart agriculture platform. By comparing the data in the database and entering the smart agriculture platform to monitor the data, it can achieve efficient and convenient management. It proposes the framework structure and system characteristics of the facility agricultural internet of things technology system. It also investigates and analyzes related products according to the system framework structure, and summarizes the current status of product development and the functions that can be achieved. According to the survey, 56 people believed that there was no after-sale service in the whole survey process. It accounts for 32.5% of the total surveyed persons, indicating the lack of local modern agricultural management. Thirty-one people are willing to buy agricultural products online, accounting for 18% of the total surveyed, indicating that the development of local online shopping is relatively backward.

UAV-Assisted Sleep Scheduling Algorithm for Energy-Efficient Data Collection in Agricultural Internet of Things

The rapid development of the agricultural Internet of Things (IoT) is inseparable from the support of wireless sensor networks (WSNs) in recent years. To further facilitate the adaptation of WSNs to agricultural applications, reducing the energy consumption of sensor nodes in agricultural environments has become a crucial problem. Sleep scheduling in randomly deployed WSNs is an effective method to reduce the energy consumption of sensor nodes, which can extend the network lifetime while ensuring network coverage. However, most of the existing sleep scheduling algorithms require frequent information exchange (such as broadcasting to find neighboring nodes and collecting nodes’ residual energy), which will inevitably lead to massive energy consumption. To address this problem, in this article, an unmanned aerial vehicle (UAV)-assisted sleep scheduling algorithm (UAVSS) is proposed, which avoids excessive information exchange among nodes and ensures sufficient network coverage with the least number of sensor nodes. The UAV traverses all the sensor nodes along the shortest path to help information exchange and gather sensing data. Furthermore, the UAV path is planned by using the latest monarch butterfly optimization (MBO) algorithm. Simulation results indicate that the proposed UAVSS can prolong the network lifetime while ensuring the required area coverage.

Viewpoints on Net-Zero Emissions of Agricultural Energy Internet

Viewpoints on Net-Zero Emissions of Agricultural Energy Internet

Crop Growth Monitoring System Based on Agricultural Internet of Things Technology

In order to improve the effect of crop growth monitoring, this paper combines the Internet of Things technology to construct a crop growth monitoring system based on the agricultural Internet of Things technology. Moreover, this paper combines the fuzzy theory to process the data of the agricultural Internet of Things to reduce the uncertainty of the data processing of the agricultural Internet of Things. After improving the fuzzy algorithm, this paper builds an intelligent model, builds the system structure framework according to the actual needs of crop growth monitoring, sets up and analyzes the environmental parameters, and builds the crop growth monitoring system from multiple perspectives. Finally, after constructing the system structure of this paper, the performance of the system proposed in this paper is verified. From the test results, it can be seen that the crop growth monitoring system based on the agricultural Internet of Things proposed in this paper has a good effect.

A Novel Unmanned Aerial Vehicle Charging Scheme for Wireless Rechargeable Sensor Networks in an Urban Bus System

Wireless sensor networks (WSNs) are implemented in many aspects of daily life, such as Internet of Things applications, industrial automation, and intelligent agriculture. Sensors are typically powered by batteries. Chargers can be used to supply power to sensor nodes and thus extend the lifetime of WSNs. This special type of network is named a wireless rechargeable sensor network (WRSN). However, due to the limited battery power and different deployment locations of the sensors, efficiently moving the chargers from the current sensor nodes to the next sensor nodes is a challenge. In this study, we propose an unmanned aerial vehicle (UAV)-based charging scheme in an urban bus system, involving the coordination between UAVs and bus schedules. The UAVs can be recharged by urban buses and then supply the power to sensor nodes. We implemented three charging strategies: naïve, shortest path, and max power. In the naïve strategy, the UAVs fly directly to sensor nodes when the sensors are lacking power. In the shortest path strategy, the minimum distance between the sensor node and bus location is calculated, and the UAVs fly the shortest path to the sensor nodes. In the maximum power charging strategy, the UAV that has the highest battery power is assigned to work. The experimental results show that the shortest path charging and max power charging strategies perform better than naïve charging in different parameter settings. To prolong the lifetime of the network system, adjusting the bus frequency according to the number of nearby sensors around the bus route is favorable.

Multiscale Layout and Planning of Smart Gardens in the Environment of Agricultural Internet of Things.

This paper studies the general architecture and key technologies of the Agricultural Internet of Things and summarizes the latest progress of the Internet of Things system model. On this basis, a general system architecture model suitable for the Agricultural Internet of Things is initially established, and the key technologies of the Agricultural Internet of Things are expounded to provide a reference for the in-depth application and development of the Agricultural Internet of Things. Aiming at the problem that the energy consumption of each node in the network is uneven in the process of monitoring agricultural production environment by wireless sensor network, which leads to the failure of some nodes in the network too fast, an event-driven wireless routing algorithm for agricultural production environment monitoring is proposed. By reducing the number of pieces of control information sent in the cluster, the algorithm selects the next hop forwarding node from candidate nodes according to the forwarding cost function to shorten the data transmission distance, reduce energy consumption, and improve the life cycle of the entire network. The experimental results show that the algorithm in this paper effectively avoids the waste of node energy consumption caused by the active transmission mode and at the same time better balances the overall energy consumption of the network and prolongs the life cycle of the network. The platform satisfies the needs of a local garden bureau to integrate comprehensive landscaping business information and display comprehensive landscaping information in a comprehensive, timely, and dynamic manner. At the same time, it has made useful explorations in the development and integration of landscaping business data interfaces and GIS data interfaces. All system functions of the smart garden information management platform project are reasonably designed and completely correct and basically meet the software function requirements specified in the requirements specification. Some functions were found not realized during the test. After rectification, various systems have been improved. All system interfaces of the smart garden information management platform project have reasonable layout, simple operation, and good ease of use. The smart garden information management platform project meets the business logic requirements and fulfills the requirements of the information construction of the Garden and Greening Administration. All systems of the smart garden information management platform project are simple to install and easy to operate and meet the user's requirements after installation. The system has a strict permission setting function. People with different permissions can only see the content of the fields that they have permission to access, which effectively ensures the security of the data.

Data Transmission Method of Farmland Production Monitoring System Based on Internet of Things

The invention discloses a data transmission method of farmland production monitoring system based on the Internet of Things, which belongs to the field of agricultural Internet of Things. The agricultural Internet of Things system includes an Internet of Things perception layer, an Internet of Things network layer and an application layer. The Internet of Things perception layer transmits the received information to the application layer through the Internet of Things network layer by wired or wireless network; A plurality of test instruments and equipment collect farmland crop data, and a plurality of wireless transmission modules collect farmland data and transmit them to the data management platform, which queries and calls farmland data in real time. In this design, the obtained field data can be imported into the storage location accurately and quickly, and the data of each site can be collected into a data management platform. It can monitor the equipment status and data quality in real time, reduce the workload of testers and improve the data transmission speed and quality.

FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things

In this paper, we propose a federated learning-based intrusion detection system, named FELIDS, for securing agricultural-IoT infrastructures. Specifically, the FELIDS system protects data privacy through local learning, where devices benefit from the knowledge of their peers by sharing only updates from their model with an aggregation server that produces an improved detection model. In order to prevent Agricultural IoTs attacks, the FELIDS system employs three deep learning classifiers, namely, deep neural networks, convolutional neural networks, and recurrent neural networks. We study the performance of the proposed IDS on three different sources, including, CSE-CIC-IDS2018, MQTTset, and InSDN. The results demonstrate that the FELIDS system outperforms the classic/centralized versions of machine learning (non-federated learning) in protecting the privacy of IoT devices data and achieves the highest accuracy in detecting attacks.

Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions

In this paper, we review and analyze intrusion detection systems for Agriculture 4.0 cyber security. Specifically, we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0. Then, we evaluate intrusion detection systems according to emerging technologies, including, Cloud computing, Fog/Edge computing, Network virtualization, Autonomous tractors, Drones, Internet of Things, Industrial agriculture, and Smart Grids. Based on the machine learning technique used, we provide a comprehensive classification of intrusion detection systems in each emerging technology. Furthermore, we present public datasets, and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0. Finally, we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.

UAV-assisted connectivity enhancement algorithms for multiple isolated sensor networks in agricultural Internet of Things

In recent years, Wireless Sensor Networks (WSNs) are used in agricultural Internet of Things (IoT) to observe crop data, that has improved the output and quality of agricultural product. However, the connectivity of WSNs in agricultural IoT will encounter new challenges when a farmland is segmented into multiple isolated sub-sensor networks (MISN) due to natural environmental factors. In order to tackle this issue, a new definition of connectivity in MISN is proposed, and the Unmanned Aerial Vehicle (UAV) assisted connectivity enhancement algorithms (UCE) are designed. Our target is to minimize energy consumption of the UAV while satisfying MISN connectivity requirements. Firstly, the destination selection ant colony optimization algorithm (DSACO) and the normalized ant colony optimization algorithm (NACO) are proposed to connect all the sub-sensor networks. Through comparative analysis of them, the optimal path for the UAV to solve the above optimization problem is found. Secondly, autonomously generated optimal point ant colony optimization algorithm (AGOP) is proposed to connect non-communicable nodes within each sub-sensor network. Simulation results show that the complexity of the three algorithms is low, and they can complete the connectivity enhancement task of a large outdoor MISN with reduced energy consumption of the UAV, and the connectivity of the MISN has been significantly improved.

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things.

The deep neural network is used to establish a neural network model to solve the problems of low accuracy and poor accuracy of traditional algorithms in screening differentially expressed genes and function prediction during the walnut endocarp hardening stage. The paper walnut is used as the research object to analyze the biological information of paper walnut. The changes of lignin deposition during endocarp hardening from 50 days to 90 days are observed by microscope. Then, the Convolutional Neural Network (CNN) and Long and Short-term Memory (LSTM) network model are adopted to construct an expression gene screening and function prediction model. Then, the transcriptome and proteome sequencing and biological information of walnut endocarp samples at 50, 57, 78, and 90 days after flowering are analyzed and taken as the training data set of the CNN + LSTM model. The experimental results demonstrate that the endocarp of paper walnut began to harden at 57 days, and the endocarp tissue on the hardened inner side also began to stain. This indicates that the endocarp hardened laterally from outside to inside. The screening and prediction results show that the CNN + LSTM model’s highest accuracy can reach 0.9264. The Accuracy, Precision, Recall, and F1-score of the CNN + LSTM model are better than the traditional machine learning algorithm. Moreover, the Receiver Operating Curve (ROC) area enclosed by the CNN + LSTM model and coordinate axis is the largest, and the Area Under Curve (AUC) value is 0.9796. The comparison of ROC and AUC proves that the CNN + LSTM model is better than the traditional algorithm for screening differentially expressed genes and function prediction in the walnut endocarp hardening stage. Using deep learning to predict expressed genes’ function accurately can reduce the breeding cost and significantly improve the yield and quality of crops. This research provides scientific guidance for the scientific breeding of paper walnut.

Smart Garden Planning and Design Based on the Agricultural Internet of Things.

To improve the effect of urban agricultural garden landscape planning and design, this paper combines the agricultural Internet of Things technology to construct a smart garden planning and design system. Moreover, this paper selects the LEACH protocol that can support monitoring for a long time according to actual application needs, introduces the latest swarm intelligence optimization algorithm, the gray wolf algorithm, to optimize some of the problems in the LEACH protocol, and conducts simulation experiments on the improved algorithm. The simulation experiment results show that the improved algorithm has obvious advantages in cluster head selection, data transmission within the cluster, and route maintenance. After constructing a smart garden planning system based on the agricultural Internet of Things, the effect of the agricultural Internet of Things data processing in this paper is evaluated. Finally, this paper constructs a garden simulation system and analyzes the performance of the system. The results verify that the agricultural Internet of Things has a good effect in the planning and design of smart gardens.

ОСОБЛИВОСТІ ВИКОРИСТАННЯ ЦИФРОВИХ ТЕХНОЛОГІЙ В АГРОБІЗНЕСІ

The article examines promising directions for the development of digital technologies in agriculture. It was determined that a feature of the digitalization of the agrarian sphere is the differentiation of land resources by productivity, the need for spot use of indicators of weather, phytosanitary, veterinary and other conditions at the local level for flexible response of business entities to their changes. It was found that digitalization of agricultural production allows to significantly reduce costs. It has been established that the European Parliament on Agriculture and Rural Development distinguishes digital technologies according to the degree of impact on agricultural production: low, medium, high. It is emphasized that digital technologies are usually used in combination: IoT, Big Data and AI, AI and robotics, drones with satellites and big data, etc. The peculiarities of using of drones by agricultural producers are revealed. Digital agriculture as a management strategy in agricultural production and the main factors of the transition of producers of agricultural products to digital agriculture are considered. It is emphasized that the use of smart technologies allows monitoring in all branches of agriculture. The advantages of using blockchain technology in agriculture are characterized. The problems that arise when using blockchain technologies are described. Trends in the development of digitization of agriculture are analyzed. Among them, such technologies as the Internet of Things, agricultural robotics, artificial intelligence, drones, precision agriculture, agricultural biotechnology, big data and analytics, controlled environment agriculture, regenerative (restorative) agriculture, high-speed Internet access are highlighted. The advantages and problems of the introduction of digital technologies in agricultural production and management are established. The tasks that must be solved when implementing digital technologies in the agricultural sector are summarized. It was emphasized that the digitalization of the agricultural sector will contribute to the development of efficient, sustainable and ecologically clean agricultural production.

MHADBOR: AI-Enabled Administrative-Distance-Based Opportunistic Load Balancing Scheme for an Agriculture Internet of Things Network

In this article, we present a supervised machine learning multipath and administrative-distance-based load balancing algorithm for an Agriculture Internet of Things (AG-IoT) network. The proposed algorithm is known as an artificial intelligence or simply AI-enabled multihop and administrative-distance-based opportunistic routing (MHADBOR) algorithm, which processes the collected information from source to the destination by means of multihop count and administrative-distance-based communication infrastructure in the network. Beside that, we used cluster heads (CH), microbase stations ($\boldsymbol {\Re BS}$ℜBS), and macrobase stations ($\boldsymbol {\aleph BS}$ℵBS) in the network with a frequent rate to effectively utilize the administrative distance while managing the deployed network traffic in a congestionless communication environment. In addition, the MHADBOR algorithm empowers the participating devices to practice the administrative distance rather than hop count communication when they are in the vicinity of network special components, e.g., CH and $\boldsymbol {\Re BS}$ℜBS outcome statistics of the MHADBOR algorithm in the simulation environment exhibit an extraordinary improvement in contention, congestion, communication, and computing costs, accompanied by throughput and end-to-end (E2E) delay and packet loss ratio in the deployed AG-IoT network.