Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence (AI)

Alreshidi, Eissa Jaber

doi:10.14569/ijacsa.2019.0100513

Cited by 117 publications

(61 citation statements)

References 35 publications

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“…This intelligent method has been applied to intelligent agriculture. Alreshidi [54] explored artificial intelligence technologies for smart, sustainable agriculture. Pantazi et al [55] used machine learning to predict wheat yield based on online multilayer soil data and satellite imagery crop growth characteristics, and showed that the supervised Kohonen networks had the best overall performance.…”

Section: Single Methodsmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

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Section: Single Methodsmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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“…• Intelligent robots based on pre-set machine learning models and computer vision technology that complete basic agricultural tasks much more quickly than manpower and traditional robots, e.g., intelligent seeding robot and intelligent harvesting robots; • Crop and soil monitoring, which have applied both computer vision technology and deep learning algorithms to analyze the crop growth data and soil health data; • Predictive analysis, which forecasts the impact of weather changes on crop harvests by training machine learning model [97]. Based on the above studies, manual operations are being replaced by intelligent machines and decision-making systems, which contribute to the development of smart agriculture [10].…”

Section: F Data Analysis Technologymentioning

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

279

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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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“…Communication for disaster management was dealt with by participating organizations, victims and affected populations, vulnerable community and areas in the traditional model [29]. But big data-driven technologies can easily handle the issues very accurately and timely [41]. Since various disaster genres such as early warning, caution instruction, and immediate interaction are connected with disaster type, phases and causes, so technology-based communication can speed up the process of disaster management [30].…”

Section: ) Crowdsourcingmentioning

confidence: 99%

Climate Change Adaptation and Resilience through Big Data

Sarker¹,

Yang²,

Lv³

et al. 2020

IJACSA

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The adverse effect of climate change is gradually increasing all over the world and developing countries are more sufferer. The potential of big data can be an effective tool to make an appropriate adaptation strategy and enhance the resilience of the people. This study aims to explore the potential of big data for taking proper strategy against climate change effects as well as enhance people's resilience in the face of the adverse effect of climate change. A systematic literature review has been conducted in the last ten years of existing kinds of literature. This study argues that resilience is a process of bounce back to the previous condition after facing any adverse effect. It also focuses on the integrated function of the adaptive, absorptive and transformative capacity of a social unit such as individual, community or state for facing any natural disaster. Big data technologies have the capacity to show the information regarding upcoming issues, current issues and recovery stages of the adverse effect of climate change. The findings of this study will enable policymakers and related stakeholders to take appropriate adaptation strategies for enhancing the resilience of the people of the affected areas. Keywords-Disaster resilience; administrative resilience; community resilience; disaster management; environmental managementAs a new paradigm, a big data approach is considered as the most effective method for taking quick and effective decisions [9]. It can analyze the huge amount of data obtained from various sources such as weather data, social media, electronic and print media, non-governmental organizations, voluntary community organizations, and various social networking sites. Big data provides a big opportunity for communication which can help susceptible community people about an upcoming threat, challenges, risks and disasters [10]. Communication provides a way to communicate with each other before, during and after a disaster to inform the condition to one another and make preparation and also acts as a source of big data [11]. Big data encourage researchers and policymakers to conduct an in-depth analysis of communication data from a mobile phone, social media and other communication devices which has big rationality for disaster resilience.

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Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence (AI)

Cited by 117 publications

References 35 publications

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

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

Climate Change Adaptation and Resilience through Big Data

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