Smart Agriculture Based on Internet of Things: Comparison
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Please note this is a comparison between Version 2 by Lindsay Dong and Version 1 by Shahidul Islam.

The Internet of Things (IoT) is a transformative technology that is reshaping industries and daily life, leading us towards a connected future that is full of possibilities and innovations. IoT technologies are widely used in the agriculture sector in many developed countries to increase production and meet the demand for food supply in the market. IoT in agriculture can reduce production costs and time by providing precision agriculture.

  • Internet of Things
  • IoT
  • smart agriculture

1. Introduction

Farmers very often encounter financial losses resulting from unforeseen natural calamities. However, with access to advanced weather forecasts through IoT technology, they might be able to avoid or mitigate these losses to a certain extent. By incorporating IoT solutions, farmers can receive real-time weather forecasts and remotely monitor their agricultural operations, which enables them to make informed decisions accordingly. Similarly, a proposed framework can empower farmers to visualize sensor data, control irrigation pumps, and optimize plant and water management practices for improved productivity and resource efficiency. Many state-of-the-art projects combining IoT and data mining techniques in the agriculture sector have been carried out to develop smart agriculture infrastructure [5][1]. The application of IoT in agriculture has brought great revolutionary changes to the agricultural environment by addressing multiple challenges and examining different complexities [6][2].

2. Smart Agriculture Based on Internet of Things

Fan et al. [8][3] proposed a system framework for establishing an intelligent agriculture platform using big data analysis and IoT sensor data via cloud technology. Andreas et al. [9][4] provide a thorough review of big data analysis in agriculture, analyzing thirty-four research papers to identify the current applications, challenges, and potential solutions. Their work highlighted the increasing availability of big data sources, tools, and techniques that can drive innovation and research for smarter farming practices, ultimately contributing to sustainable agriculture and higher-quality food production. A system structure was developed in the article [10][5] to improve the combination of big data and artificial intelligence in agriculture, where data from IoT sensors was received and stored in the cloud to monitor the farm. They created a control system based on data management and node sensors in crop fields for smartphones and online applications. In the article [11][6], a system framework was created and built. The framework consisted of three components: a control box, a web application, and a mobile application. Their method was put in place to regulate crop irrigation and govern agricultural plots. The solenoid valve switching procedure by the farmer is controlled by a smartphone app. A survey of the literature was centered on studies and analyses of the application of IoT in modern farming [12][7]. Their research and analysis showed how China can reduce human effort in agriculture by relying on IoT technology. They presented some categories by analyzing agricultural system development. By explaining the architecture and applications of cloud technologies, the researchers in [13][8] focused on the importance of using IoT and cloud computing in the agricultural sector. This layered architecture, in conjunction with Radio Frequency Identification (RFID) technology, is used to automate planting and production. Doshi et al. [14][9] proposed an IoT technology that generates messages from their applications to instruct farmers to suggest smart farming.
As surveyed in the scientific article [15][10], IoT has been used in a variety of investigations in recent years. They reviewed modern farm technology and explored a variety of live monitoring systems for IoT-based applications and wireless sensor networks. They also discussed well-known technologies that are continually pushing the IoT to improve. They also listed some of the obstacles we may face when working in agriculture with IoT, including hardware constraints, networking challenges, technical concerns, resource optimization, and mobility. Thise systematic review [16][11] delves into the integration of cutting-edge technologies like predictive modeling algorithms, deep-learning-based sensing, and big urban data in shaping immersive digital twin cities. By analyzing the recent literature, the paper establishes the significance of virtual simulation tools, spatial cognition algorithms, and multi-sensor fusion technology in developing sustainable urban governance networks and data-driven smart city environments. The study provides valuable insights into the role of the Internet of Things, digital twin modeling, and intelligent sensing devices in building smarter and more connected urban infrastructures. The work proposed by Nandan et al. [17][12] provides a literature review by illustrating how climate change affects the agriculture and food security of the Barisal district in Bangladesh. Here, they discussed the environmental condition of the Barisal district and the impact of rainfall, drought, waterlogging, thunderstorms, excessive fog, and climate change on agriculture production. The authors of [18][13] presented constructive research on the overall status of technology-dependent agriculture in Bangladesh. A quality-aware autonomous information system for agriculture services based on agriculture-related data was developed in the article [19][14]. A literature review on the role of Internet of Things technologies in agriculture that explored the varied effects of IoT in agriculture, the benefits and drawbacks of IoT devices, and the application layer required for farming in current technology was introduced in article [20][15]. The authors of [21][16] suggested a smart agriculture system design that enhanced a smart farming system for effective management and control of agricultural greenhouses through IoT and data mining technology to increase production in agriculture. They employed IoT technology to collect a large amount of environmental information from grain greenhouses and used advanced algorithms to pick relatively favorable data as a clustering method for environmental reference data.
Thomas et al. [22][17] addressed the various systems, frameworks, and multiple sources for smart farming. They emphasized the utilization of cloud computing and big data technology in the development of existing agricultural event systems. An alert system was proposed in [23][18] that presents a system framework capable of controlling the amount of water passing through IoT devices in agriculture. Said et al. [24][19] proposed a method to determine the minimum amount of irrigation and the maximum amount of water used on the plants through an intelligent irrigation plan. By keeping an eye on the water position and irrigation schedule of the tomato crop in extremely dry climate conditions, this approach sought to investigate the efficacy of the Intelligent Irrigation System (IIS) related to Water Use Efficiency (WE) and Irrigation Water Use Efficiency (IWU) and determine its viability.
The authors of [25][20] discussed a proposed framework that aims to balance energy efficiency and security in precision agriculture. The framework uses hashing as the only form of advanced encryption, which adds an extra layer of security to the public channel. Unlike existing management systems, this proposed method does not store public keys. By allowing on-field sensors to not be directly connected to the sink node, the proposed system provides significant residual energy savings. Compared to the current aggregation strategy, the suggested scheme results in about 35% more alive nodes and 32% greater retention of residual energy. The authors of [26][21] proposed a trust management approach for ensuring the security of smart agriculture in the cloud-based Internet of Agriculture Things (IoAT). The authors suggest that the integration of cloud computing with the IoAT can significantly improve the efficiency of agriculture, but it also poses security challenges such as data privacy, integrity, and authenticity. The AgriTrust approach (a trust management mechanism that substitutes for conventional cryptography methods) consists of three main components: a trust model, a trust evaluation mechanism, and a trust management mechanism. The trust model defines the trustworthiness of entities in the IoAT, such as devices, sensors, and cloud servers. The trust evaluation mechanism is used to evaluate the trustworthiness of entities based on their past behavior and feedback from other entities. The authors of [27][22] propose an IoT-based WSN framework that provides an efficient and secure solution for smart agriculture applications. The proposed scheme’s use of a hierarchical architecture, data aggregation and compression techniques, and secure data transfer protocols can significantly improve the efficiency and security of smart agriculture applications. The proposed framework consists of three tiers: the sensor layer, the intermediate layer, and the application layer. Additionally, they have proposed a secure data transfer protocol that makes use of Elliptic Curve Cryptography (ECC) and the Advanced Encryption Standard (AES) to ensure the security of the data transmitted between the sensor and the application layer.

References

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