Photovoltaic Agricultural Internet of Things Towards Realizing the Next Generation of Smart Farming

Huang, Kai; Shu, Lei; Li, Kailiang; Yang, Fan; Han, Guangjie; Wang, Xiaochan; Pearson, Simon

doi:10.1109/access.2020.2988663

Cited by 49 publications

(30 citation statements)

References 32 publications

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“…In addition, because a large area of land is required for both field agricultural production and photovoltaic power generation, deploying photovoltaic panels over farmland can optimize land usage [67]. Moreover, sensors for monitoring field crops can acquire power from a photovoltaic system and monitor photovoltaic power generation facilities [68]. However, this technology also brings some security challenges.…”

Section: B Agricultural Internet Of Things Applicationmentioning

confidence: 99%

A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

Yang

Shu

Chen

et al. 2021

IEEE/CAA J. Autom. Sinica

Self Cite

247

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With the deep combination of both modern information technology and traditional agriculture, the era of agriculture 4.0, which takes the form of smart agriculture, has come. Smart agriculture provides solutions for agricultural intelligence and automation. However, information security issues cannot be ignored with the development of agriculture brought by modern information technology. In this paper, three typical development modes of smart agriculture (precision agriculture, facility agriculture, and order agriculture) are presented. Then, 7 key technologies and 11 key applications are derived from the above modes. Based on the above technologies and applications, 6 security and privacy countermeasures (authentication and access control, privacy-preserving, blockchain-based solutions for data integrity, cryptography and key management, physical countermeasures, and intrusion detection systems) are summarized and discussed. Moreover, the security challenges of smart agriculture are analyzed and organized into two aspects: 1) agricultural production, and 2) information technology. Most current research projects have not taken agricultural equipment as potential security threats. Therefore, we did some additional experiments based on solar insecticidal lamps Internet of Things, and the results indicate that agricultural equipment has an impact on agricultural security. Finally, more technologies (5G communication, fog computing, Internet of Everything, renewable energy management system, software defined network, virtual reality, augmented reality, and cyber security datasets for smart agriculture) are described as the future research directions of smart agriculture.

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Section: B Agricultural Internet Of Things Applicationmentioning

confidence: 99%

A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

Yang

Shu

Chen

et al. 2021

IEEE/CAA J. Autom. Sinica

Self Cite

247

View full text Add to dashboard Cite

show abstract

“…Regardless of the type of equipment and technology used, the transmission of this information will consume a large amount of energy and the energy supply will be a bottleneck for future smart agriculture. Photovoltaic Agricultural Internet of Things (PAIoT) integrated agricultural production with renewable energy power generation and control through an IoT platform [ 130 ]. PAIoT can realize the docking of supply and demand through sharing information and then photovoltaic generation can match multiple types of agricultural power demands.…”

Section: Key Technologymentioning

confidence: 99%

A Review of Key Technologies and Trends in the Development of Integrated Heating and Power Systems in Agriculture

Zhou

Yang

et al. 2021

Entropy

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Petroleum agriculture, characterized by mechanization and chemistry, is developing rapidly in China. However, petroleum agriculture has not only brought food safety problems, but also caused great obstacles to the sustainable development of society. In view of the disadvantages of oil agriculture, we provide an upgrading plan for energy systems in agriculture. This work can help reduce carbon emissions and improve food security. We introduce the most advanced technologies in Chinese agricultural development and the technical scope includes new agricultural energy power generation, agricultural energy use and the safe operation of agricultural energy systems. We describe the detailed data of agricultural bioenvironmental and energy engineering to clarify the level of agricultural energy efficiency in China. The overall conclusion of this paper is that the deep integration of agriculture and energy internet has become the development trend of agricultural energy systems.

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“…Another issue is the greenhouse crop diseases identification and early classification; recently, attempts have been made to design efficient approaches based on the IoT technique and image processing [17], for example, Mishra et al [18] designed an IoT-based strawberry disease prediction system for smart farming; the capability of the model in disease prediction was shown. Kim et al [19] used the IoT technique and a machine learning algorithm to classify plant diseases at an early stage.…”

Section: Introductionmentioning

confidence: 99%

Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks

et al. 2021

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To support farmers and improve the quality of crops production, designing of smart greenhouses is becoming indispensable. In this paper, a novel prototype for remote monitoring of a greenhouse is designed. The prototype allows creating an adequate artificial environment inside the greenhouse (e.g., water irrigation, ventilation, light intensity, and CO2 concentration). Thanks to the Internet of things technique, the parameters controlled (air temperature, relative humidity, capacitive soil moisture, light intensity, and CO2 concentration) were measured and uploaded to a designed webpage using appropriate sensors with a low-cost Wi-Fi module (NodeMCU V3). An Android mobile application was also developed using an A6 GSM module for notifying farmers (e.g., sending a warning message in case of any anomaly) regarding the state of the plants. A low-cost camera was used to collect and send images of the plants via the webpage for possible diseases identification and classification. In this context, a deep learning convolutional neural network was developed and implemented into a Raspberry Pi 4. To supply the prototype, a small-scale photovoltaic system was built. The experimental results showed the feasibility and demonstrated the ability of the prototype to monitor and control the greenhouse remotely, as well as to identify the state of the plants. The designed smart prototype can offer real-time remote measuring and sensing services to farmers.

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Photovoltaic Agricultural Internet of Things Towards Realizing the Next Generation of Smart Farming

Cited by 49 publications

References 32 publications

A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

A Review of Key Technologies and Trends in the Development of Integrated Heating and Power Systems in Agriculture

Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks

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