Agricultural Internet Of Things Research Articles

A systematic review of IoT technologies and their constituents for smart and sustainable agriculture applications

Due to the world's rapid population expansion, the demand for food is anticipated to increase significantly during the coming decade. Traditional farming practices cannot meet the need for the food crop. Conventional farming methods use resources like land, water, herbicides, and fertilisers rather inefficiently. When it comes to making the most effective and sustainable use of resources to increase production, automation in agriculture is garnering a lot of interest. How people and machines operate on farms has been changed by integrating the Internet of Things (IoT) with numerous sensors, controllers, and communication protocols. A comprehensive literature review of the key technologies involved in smart and sustainable agriculture, viz. various sensors, controllers, communication standards, IoT based intelligent machinery, were compared and presented. These sensors were continuously producing a significant quantity of data on the agricultural field. These data were transmitted to the central control unit for analysis to meet the demands for water, fertiliser, pesticides, etc. The architecture and importance of data analytics in agriculture IoT, case studies of current agricultural automation utilising IoT, key challenges and open issues in agriculture IoT technology were discussed. The findings provide support for the selection of IoT technologies for specific applications.

SAgric-IoT: An IoT-Based Platform and Deep Learning for Greenhouse Monitoring

The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes regarding field conditions and not purely based on experience, thus minimizing the wastage of supplies (seeds, water, pesticide, and fumigants). On the other hand, CNN complements monitoring systems with tasks such as the early detection of crop diseases or predicting the number of consumable resources and supplies (water, fertilizers) needed to increase productivity. This paper proposes SAgric-IoT, a technology platform based on IoT and CNN for precision agriculture, to monitor environmental and physical variables and provide early disease detection while automatically controlling the irrigation and fertilization in greenhouses. The results show SAgric-IoT is a reliable IoT platform with a low packet loss level that considerably reduces energy consumption and has a disease identification detection accuracy and classification process of over 90%.

Agricultural IoT Data Storage Optimization and Information Security Method Based on Blockchain

Given the issues of low efficiency of agricultural Internet of Things (IoT) data collection and data storage security, this study proposes a fast and reliable storage method for IoT data based on blockchain. Firstly, it performs RC5 encryption for data in the IoT sensor module. Secondly, it aggregates the same batch of collected data in the gateway into a transaction and reconstructs the Merkle ordered tree to verify the data integrity. Finally, it modifies the configuration rules of blockchain to improve the efficiency of blockchain data storage. Compared with experimental results for hash values of blockchain storage data and the stored data itself in the blockchain, the proposed method has significant advantages in data writing, and its efficiency in data reading was nearly 10 times higher than the other methods. At the same time, the method has the advantages of confidentiality, integrity, availability, controllability and non-repudiation of information security. The study can provide a solution for efficient collection and secure storage of agricultural IoT data, and it can provide technical support for realizing decentralized agricultural IoT data collection.

DynGATT: A dynamic GATT-based data synchronization protocol for BLE networks

Bluetooth Low Energy (BLE) is a wireless communication technology for power-constrained Internet of Things (IoT) applications. BLE data can be transmitted via either the IPv6 or the Generic ATTribute (GATT) Profile protocol, with the former supporting dynamic IoT structures and the latter being application-friendly. In fact, GATT requires the data layout to be known in advance by peer devices, in order to properly interpret the received data. In this paper, we introduce DynGATT, a protocol that achieves the benefits of both IPv6 and GATT, by extending GATT in a seamless fashion to support dynamic IoT structures. The key idea of DynGATT is to use GATT descriptors, originally intended to specify data in static IoT scenarios, to also specify IoT systems whose structures may dynamically evolve. Peer devices reading these descriptors will know how to interpret the data of GATT characteristics provided by devices joining the IoT network. Because no additional data have to be transmitted, the connection time is then reduced with respect to classical BLE. DynGATT has been implemented and tested in an agricultural IoT application, with different types of sensor nodes. Our experimental evaluation shows that DynGATT is very power-efficient, despite its added flexibility. Its worst-case power consumption is only around 19.37µA per data transmission and around 41.37µA overall. This consumption can be further reduced by using the methods discussed in this paper. To the best of our knowledge, this work is the first to support dynamic IoT structures in a GATT-based setting.

Dynamic evaluation of a post-pandemic agricultural traceability system, based on the HFLTS-DEMATEL method

PurposeDue to consumption changes in the post-pandemic era, the production safety of agricultural products is affecting global consumers. This paper constructs an evaluation index of the agricultural Internet of things (IOT) traceability system and evaluates it using the dynamic hesitant-fuzzy linguistic term sets (HFLTS)-based DEMATEL method to improve agricultural supply-chain links and improve production quality.Design/methodology/approachThe agricultural IOT traceability index system is constructed using the literature and expert interviews; it comprises 6 first-level indices and 20 second-level indices. The agricultural IOT traceability system is evaluated using the dynamic HFLTS-DEMATEL method.FindingsProducers' awareness of agricultural-production safety (A11) has the most significant impact on production and processing links, while warehouse location and storage capacity (A31) have the largest impact on the circulation link. Inspection authenticity and transparency and quarantine information (A41) have the largest impact on the detection-consumption link. The extent to which the traceability-platform construction is complete (A62) has the largest impact on technical support.Research limitations/implicationsThe present paper may be limited to the era of post-pandemic, and it is hard to consider all the indices. Further research can broaden the research context and establish a more comprehensive index system.Practical implicationsThe index system constructed in this study will surely help relevant regulatory authorities in China to promote the construction of agricultural IOT traceability system and establish a unified standard, so as to provide a basis for future developers to enter the field. Accordingly, it also can help every subject to identify the key indices of each process in the agricultural-product supply chain and guide relevant departments to conduct targeted information tracking and management. The consumers could also understand the standards of traceable agricultural products and effectively protect their own rights and interests.Originality/valueThe existing literature does not provide an objective, unified standard for measuring a decentralized traceability system or identifying key processes. This study therefore proposes a new evaluation index system and uses a dynamic evaluation method to determine the importance of key indices. This study identifies the most important indices in each process, making it possible to discover, improve, and enhance the quality of agricultural products at a practical level.

Using internet of things technologies to optimize agriculture and increase productivity

The review presented in this article is devoted to the application of Internet of Things (IoT) technologies in agriculture and its impact on the productivity and sustainability of this industry. During the study, comprehensive analyses were carried out, including monitoring of climatic conditions, soil conditions, and animal activity, using IoT sensor networks. The results showed that the introduction of IoT in agriculture significantly improves resource management, which leads to increased productivity and reduced costs. However, the study also identified challenges such as data cybersecurity and the need for staff training to work with IoT systems. Despite these challenges, the use of IoT is a promising solution for agriculture, in a changing climate and growing demand for products. According to the results of the study, it can be concluded that IoT has the potential to transform agriculture, making it more sustainable, efficient and environmentally sustainable.

Determinants of the Internet of Things adoption by millennial farmers

<abstract> <p>Indonesia is experiencing difficulties in ensuring the sustainability of the agricultural system as the younger generation experiences reluctance to enter the business of agriculture. Smart farming is believed to be a solution to the difficulty of millennials entering the business of agriculture. One of the main elements of smart farming is the Internet of Things (IoT). This study aims to determine the factors that encourage millennial farmers in Central Java to adopt IoT-based innovations using a behavioral reasoning theory (BRT) perspective. Data were collected from 120 millennial farmers in Central Java; we applied BRT, an analytical technique, to examine IoT adoption by millennial farmers. Primary survey data analysis was carried out by applying structural equation modeling techniques. The results showed that millennial farmers accepting the adoption of IoT technology is a factor of relative advantage and social influence. Meanwhile, the reason for rejecting the adoption of IoT technology is technology anxiety. This research provides information on the reasons for accepting and reasons for rejecting the adoption of IoT in agriculture by millennial farmers in Central Java province, which will be helpful for the government in the design of a program to attract millennials to go into business in agriculture.</p> </abstract>

IOT Based Smart Farming Application

Smart agriculture is one of the Internet of Things' most important uses. Water, fertilizer, and crop yield waste are all reduced via smart agriculture. The manual detection of specifications like temperature, moisture, and humidity in the existing agricultural system drives up labor costs, and continuous monitoring is not possible. The irrigation procedure is carried out automatically in this study utilizing various sensors, which reduces manual work. It is suggested to utilize a sensor-based monitoring system for crop fields. It would entail gathering information on the soil moisture, humidity, and temperature. Automation of irrigation is possible by keeping an eye on all these variables. Unquestionably, smart farming is a key facilitator in providing more food with less resources for a growing global population. While this is essential to feeding the world's expanding population responsibly, smart farming also offers producers and communities throughout the world additional advantages. Farmers may raise yields and improve environmental management by using these strategies. By monitoring the field, IoT-based smart agriculture enhances the overall farming system. The Internet of Things in agriculture helps farmers save time and lessen the usage of resources like water thanks to sensors and connections. electricity, internet-connected temperature monitoring.

Blockchain-Enabled Authenticated Key Agreement Scheme for Mobile Vehicles-Assisted Precision Agricultural IoT Networks

Precision farming has a positive potential in the agricultural industry regarding water conservation, increased productivity, better development of rural areas, and increased income. Blockchain technology is a better alternative for storing and sharing farm data as it is reliable, transparent, immutable, and decentralized. Remote monitoring of an agricultural field requires security systems to ensure that any sensitive information is exchanged only among authenticated entities in the network. To this end, we design an efficient blockchain-enabled authenticated key agreement scheme for mobile vehicles-assisted precision agricultural Internet of Things (IoT) networks called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AgroMobiBlock$ </tex-math></inline-formula> . The limited existing work on authentication in agricultural networks shows passive usage of blockchains with very high costs. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AgroMobiBlock$ </tex-math></inline-formula> proposes a novel idea using the elliptic curve operations on an active hybrid blockchain over mobile farming vehicles with low computation and communication costs. Formal and informal security analysis along with the formal security verification using the Automated Validation of Internet Security Protocols and Applications (AVISPA) software tool have shown the robustness of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AgroMobiBlock$ </tex-math></inline-formula> against man-in-the-middle, impersonation, replay, physical capture, and ephemeral secret leakage attacks among other potential attacks. The blockchain-based simulation on large-scale nodes shows the computational time for an increase in the network and block sizes. Moreover, the real-time testbed experiments have been performed to show the practical usefulness of the proposed scheme.

IoT-Smart Agriculture: Comparative Study on Farming Applications and Disease Prediction of Apple Crop using Machine Learning

Recently, the Internet of Things has emerged as an encouraging technology that is scaling up new heights towards the modernization of real word physical objects into smarter devices in several domains. Internet of Things (IoT) based solutions in agriculture drives farming into a smart way through the proliferation of smart devices to enhanced production with minimal human involvement. This paper presents a comprehensive study of the role of IoT in prominent applications of farming, wireless communication protocols, and the role of sensors in precision farming. In this research article, the existing frameworks in IoT-based agriculture systems with relevant technologies are presented. Furthermore, the comparative analysis of the apple disease prediction system concerning different types of disease found in the apple crop are discussed. In addition, this paper presents the contributions made by numerous researchers over the past few years in the apple disease prediction system. The aim of this research is to support the development of smart agriculture applications that would be helpful in precision farming for the optimization of resources with the help of IoT in agriculture and early disease prediction in apple crops.

A trusted IoT data sharing and secure oracle based access for agricultural production risk management

Agricultural risks associated with weather events, soil conditions, diseases, and pests have risen in recent years due to climate change. These risks have burdened farmers with the shock of financial stress and endangered the region’s food security. Despite increasing risks, notable risk management tools like agricultural insurance have not effectively reached marginal farmers in developing economies. This can be attributed to the fact that existing centralized agricultural information systems lack trust factors in sharing and accessing agricultural risk data, which is accompanied by processing delays in insurance payouts. To this end, the emergence of promising technologies like blockchain helps establish trust and automates insurance-based payments with smart contracts. This work proposes a framework called AgriSSIOracle with two key contributions. The first is to have a trusted agricultural Internet of Things (IoT) data sharing that employs blockchain-based Self-Sovereign Identity (SSI) and provable credentials. Second is the decentralized oracle-based access control mechanism for smart contracts in agricultural insurance. A method for privacy-preserved authentication and data provenance for agricultural IoT devices is developed with SSI-based Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). Next, the IoT data sharing scheme is illustrated using publish–subscribe and request–response communication patterns based on the device data credentials. Finally, the decentralized oracle mechanism for smart contracts is built based on multiparty computation for risk-related data access. The implementation of the framework is realized using a permissioned identity blockchain called Hyperledger Indy and Ethereum, which is a public blockchain for smart contracts. The security and privacy analysis confirms that the AgriSSIOracle framework ensures trust factors, particularly authenticity, privacy, data provenance, data integrity, and access control. The framework is evaluated with respect to the transaction throughput, latency, credential issue-verify-revoke time, and resource utilization metrics. The evaluation results prove that the AgriSSIOracle solution is scalable and efficient and meets the requirements of real-time deployment.

Research on site selection of agricultural internet of things nodes based on rapid terrain sampling

With the development of Internet of Things (IoT) technology, modern agriculture is moving in the direction of 4.0. The Agricultural IoT is inseparable from wireless communication. However, in traditional agricultural IoT router and gateway site selection, the influence of the actual terrain on transmission loss is not considered, which results in node power wastage and increased maintenance costs. Based on a multi-sensor fusion algorithm, a fast terrain sampler is designed in this study to collect point-cloud data of the experimental site terrain. A reasonable objective function is then designed under the premise of consideration of the electromagnetic wave free-space and diffraction losses, and the locations of the routers and gateway are optimized based on k-means and particle swarm optimization (PSO) algorithm. Simulations show that the running time of the PSO algorithm is very sensitive to the changes in the execution parameters, and the improved PSO algorithm converges faster than the genetic algorithm (GA) in all three initialization methods. After collecting field terrain data, five interpolation methods were compared, and the nearest-neighbor algorithm is used to obtain the terrain model. On-site collection of received signal-strength indication (RSSI) shows that the communication quality of the optimal point selected via this algorithm is significantly higher than those of nearby points. At the same time, it is proved that the RSSI data has serious discontinuities, so the traditional gradient descent method is not suitable for solving the objective function in this work. Therefore, the algorithm used herein is of great significance for the site selection of agricultural Internet of Things nodes. However, since there are many ways to calculate the diffraction loss, the objective function of this work still needs more correction studies. The tool proposed in this work can obtain a 3D model of farmland faster than traditional surveying and mapping methods. With future research, the 3D model may be applied to soil moisture analysis, rainfall and moisture direction inversion, and plant light exposure prediction.

Agricultural Production System Based On IOT

Internet of things (IoT) is not a single word, but it has gathered billions of devices in the same lane. The Internet of things has given the lives of things. Machines have a sense now like a human. It works remotely as the program has been settled inside the chip. The system has become so smart and reliable. The Internet of things has brought out changes in most of the sectors of humankind. Meanwhile, agriculture is the main strength of a country. The more the production of agricultural products increased, the world will be more completeness from food shortage. The production of agriculture can be increased when the IoT system can be entirely implemented in the agricultural sector. Most of the approaches for IoT based agriculture have been reviewed in this paper. Related to IoT based agriculture, most of the architecture and methodology have been interpreted and have been critically analyzed based on previous related work of the researchers. This paper will be able to provide a complete idea with the architecture and methodology in the field of IoT based agriculture. Moreover, the challenges for agricultural IoT are discussed with the methods provided by the researchers.

Framing the response to IoT in agriculture: A discourse analysis

CONTEXTA technology in smart farming, the Internet of Things (IoT), is predicted to continue altering farm life by introducing opportunities and obstacles. However, there are limited studies on how farmers' views of IoT influence their decision-making regarding technology adoption. OBJECTIVETo understand what characterises farmers' experiences with IoT, we conducted a discourse analysis of 32 interviews with farmers in Ontario. METHODSDiscourse analysis was used to understand the range of meanings associated with IoT by farmers. RESULTS AND CONCLUSIONSWe find that two main discourses are present (1) the extent to which IoT was viewed as useful/helpful vs not useful/unhelpful and (2) the extent to which IoT was viewed as being their choice. The results indicate that farmers respond to IoT in four categories: embrace, accept, ignore, and caution. SIGNIFICANCEThis paper contributes to the literature by categorising the farmers' responses to IoT implementation and highlighting why farmers adopt these categories. Current literature recognizes that diagnosing the current readiness and use of innovations is a proxy for their readiness to scale. Understanding how farmers view opportunities enabled by IoT and how they experience the diffusion of IoT is a foundation for suggesting recommendations for technology improvement and development in agriculture.

The Usage of Internet of Things in Agriculture: The Role of Size and Perceived Value

The application of internet of things (IoT) has reached all fields and industries with variation among countries. One of the industries that received less attention is the agriculture. This research intended to identify the factors that affect the intention to use IoT (IUIoT) among farmers in developing countries such as Iraq. Based on technology acceptance model (TAM) and theory of planned behaviour (TPB), this study proposed that perceived usefulness (PU), perceived complexity (PC), subjective norms (SN), reliability (RE) and cost saving (CS) will affect the IUIoT. Perceived value (PV) is proposed as a mediator while land size is proposed as a moderator. The data of this study was collected from 223 farmers in Iraq using purposive sampling. The analysis of Smart PLS showed that the effect of PU, PC, RE, SN, and CS on IUIoT are significant. PV mediated fully the effect of PC and partially the effect of other variables on IUIoT. Land size did not moderate the effect of PV on IUIoT. Decision makers are recommended to ease the process of using the IoT and to enlighten farmers about the benefits of using the IoT.

Research on the Construction of Marketing Mode of Agricultural Products in Hunan Province under the Environment of “Internet +”

Implementing the rural revitalization strategy is an inevitable requirement for solving the contradiction between the people’s ever-growing needs for a better life and unbalanced and inadequate development. At present, Internet technology is accelerating the extension and penetration of agriculture and rural areas, and has achieved remarkable results in agricultural technology promotion, market information services, agricultural and rural e-commerce, precision poverty alleviation and other aspects. Agricultural Internet of things and big data also show very broad application prospects. Hunan, as the hometown of fish and rice and a large agricultural province, is an important national food production base and high-quality agricultural product supply base, which has assumed great responsibility in ensuring national food security. Therefore, the “Internet + agricultural products” marketing of Hunan Province is the key to achieving rural revitalization.

Research on the Mechanism and Optimization Path of Agricultural Internet of Things Practice based on Structural Equation Model

Agricultural Internet of Things (IoT) technology is the cornerstone of China's comprehensive implementation of rural revitalization strategy, which has a profound impact on China's rural construction and modernized and intelligent agricultural industry, and is an important driving force to promote the change of "three rural areas". This study takes Anhui Province as an example, and obtains accurate data on the application mode of agricultural IOT and the assessment level of modern agriculture development through relevant surveys, and uses SEM structural equation model to hypothesize and verify the mechanism model of agricultural IOT in modern agriculture. Then the current problems of agricultural IoT development are proposed according to the model, and finally specific policy suggestions are proposed from three levels: government, enterprises and farmers to promote agricultural IoT development and rural revitalization.

Scheduling Method for Agricultural IOT Business Based on Improved Multiobjective Evolutionary Algorithm

In the modern society where technology is advancing every day, the agricultural industry is also undergoing innovation, and the Internet of Things (IoT) based on machine learning algorithms adds new vitality and yields increasing directions to this ancient industry. This study analyzes and processes data based on improved multiobjective algorithms for the application of IoT in agriculture and establishes the relevant algorithmic models. The components of IoT are introduced, and it is determined that information flow, capital flow, logistics, and Internet are the main reasons why it can be generated. After establishing an improved multiobjective evolutionary algorithm model with good convergence and diversity, the embedded multichannel sensor collection device measured in this experiment in the same cultivated environment has a more stable collection data cycle compared to the external sensor. The embedded multichannel sensor has better stability, so this sensor is selected for this study to monitor parameters such as soil moisture content and oxygen content. The IoT requires timely communication and consultation among users, and the actual experiment found that the use of ultrashort waves with a frequency of 230 MHz is the most stable and efficient.

IoT for Agricultural Information Generation and Recommendation: A Deep Learning-Based Approach

Agriculture is the foundation of national economy. Therefore, countries all over the world—developed and developing countries—attach great importance to the sustainable development of agriculture. With the rapid development of Internet of Things (IoT) technology, advance applications are being designed to enhance agricultural economy. With the application of IoT, the production mode of traditional agriculture has been restructured and rationalized. Based on the applications of IoT in agriculture, this paper presents a method to automatically classify and recommend agricultural information. The standard domain-related theories and information service system are exploited to promote IoT technology in the construction of agricultural informatization. A convolutional neural network (CNN) model is used to classify agricultural information based on the vector file generated after preprocessing textual agricultural data. With the clustering method, the influence of unbalanced number of documents in the dataset is minimized. Finally, an information recommendation method based on multimodal interaction behavior is proposed for agricultural information recommendation. Potential features from textual information are extracted which are then fed to long short-term memory (LSTM) in connection with the interaction behavior. LSTM is used for the prediction of the possibility of interaction with respect to the information recommendations system. The experimental results show the feasibility of CNN in agricultural information classification problem. A commendable clustering accuracy is obtained for the agriculture category containing a large number of documents. However, the category with fewer documents is less clustered. The model may be used to effectively extract and classify agricultural information and has great significance in structuring and shaping agricultural information for convenient use in agricultural decision-making.

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.