Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies

IEEE/CAA Journal of Automatica Sinica, Год журнала: 2021, Номер 8(4), С. 718 - 752

Опубликована: Март 10, 2021

This paper presents a comprehensive review of emerging technologies for the internet things (IoT)-based smart agriculture. We begin by summarizing existing surveys and describing emergent agricultural IoT, such as unmanned aerial vehicles, wireless technologies, open-source IoT platforms, software defined networking (SDN), network function virtualization (NFV) cloud/fog computing, middleware platforms. also provide classification applications agriculture into seven categories: including monitoring, water management, agrochemicals applications, disease harvesting, supply chain practices. Moreover, we taxonomy side-by-side comparison state-of-the-art methods toward management based on blockchain technology IoTs. Furthermore, present real projects that use most aforementioned which demonstrate their great performance in field Finally, highlight open research challenges discuss possible future directions

Язык: Английский

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Enhancing smart farming through the applications of Agriculture 4.0 technologies

International Journal of Intelligent Networks, Год журнала: 2022, Номер 3, С. 150 - 164

Опубликована: Янв. 1, 2022

Agriculture 4.0 represents the fourth agriculture revolution that uses digital technologies and moves toward a smarter, more efficient, environmentally responsible sector. Agricultural have emerged to enhance sustainability discover effective farm methods. This encompasses all digitalisation automation processes in business our daily lives, including Big Data, Artificial Intelligence (AI), robots, Internet of Things (IoT), virtual augmented reality. These technological advancements are having profound impact on lives. From technical standpoint, it brings us precision agriculture. provides data-driven strategy for efficiently growing maintaining crops cultivable land, enabling farmers use most resources at their disposal. Throughout supply chain, operations create massive volumes data. Most this information was previously untouched, but with help big data technologies, such can be used improve performance production any crop. Depending crop type its growth needs, digitised harvesters handle huge areas various situations, particularly paper is brief about condition. Smart farming, Various key specific domains Exploring Domain discussed detail and, finally, identified significant applications technologies. essential lives since they simplify duties without recognising them. In systems, fleets equipment employ current infrastructures like cloud computing connect, identify processing condition different regions requirement input materials coordinate machinery.

Язык: Английский

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Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey

IEEE/CAA Journal of Automatica Sinica, Год журнала: 2021, Номер 9(3), С. 377 - 391

Опубликована: Сен. 13, 2021

With the booming of cyber attacks and criminals against cyber-physical systems (CPSs), detecting these remains challenging. It might be worst times, but it best times because opportunities brought by machine learning (ML), in particular deep (DL). In general, DL delivers superior performance to ML its layered setting effective algorithm for extract useful information from training data. models are adopted quickly CPS systems. this survey, a holistic view recently proposed solutions is provided attack detection context. A six-step driven methodology summarize analyze surveyed literature applying methods detect The includes scenario analysis, identification, problem formulation, model customization, data acquisition training, evaluation. reviewed works indicate great potential through modules. Moreover, excellent achieved partly several high-quality datasets that readily available public use. Furthermore, challenges, opportunities, research trends pointed out future research.

Язык: Английский

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IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges

Applied Sciences, Год журнала: 2022, Номер 12(7), С. 3396 - 3396

Опубликована: Март 27, 2022

The growth of the global population coupled with a decline in natural resources, farmland, and increase unpredictable environmental conditions leads to food security is becoming major concern for all nations worldwide. These problems are motivators that driving agricultural industry transition smart agriculture application Internet Things (IoT) big data solutions improve operational efficiency productivity. IoT integrates series existing state-of-the-art technologies, such as wireless sensor networks, cognitive radio ad hoc cloud computing, data, end-user applications. This study presents survey demonstrates how can be integrated into sector. To achieve this objective, we discuss vision IoT-enabled ecosystems by evaluating their architecture (IoT devices, communication storage, processing), applications, research timeline. In addition, trends opportunities applications also indicate open issues challenges agriculture. We hope findings will constitute important guidelines promotion aiming productivity quality sector well facilitating towards future sustainable environment an agroecological approach.

Язык: Английский

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The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies

Critical Reviews in Food Science and Nutrition, Год журнала: 2022, Номер 63(23), С. 6547 - 6563

Опубликована: Фев. 3, 2022

Climate change, the growth in world population, high levels of food waste and loss, risk new disease or pandemic outbreaks are examples many challenges that threaten future sustainability security planet urgently need to be addressed. The fourth industrial revolution, Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development successful catalyst tackle critical global challenges. This review paper summarizes most relevant 4.0 technologies including, among others, digital (e.g., artificial intelligence, big data analytics, Internet Things, blockchain) other technological advances smart sensors, robotics, twins, cyber-physical systems). Moreover, insights into trends (such as 3D printed foods) have emerged result revolution will also discussed Part II this work. significantly modified industry led substantial consequences environment, economics, human health. Despite importance each mentioned above, ground-breaking solutions could only emerge by combining simultaneously. Food era characterized challenges, opportunities, reshaped current strategies prospects production consumption patterns, paving way move toward 5.0.

Язык: Английский

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Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0

Electronics, Год журнала: 2021, Номер 10(11), С. 1257 - 1257

Опубликована: Май 25, 2021

Smart Agriculture or Agricultural Internet of things, consists integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality productivity agricultural products. The convergence Industry 4.0 Intelligent provides new opportunities for migration from factory agriculture future generation, known as 4.0. However, since deployment thousands IoT based devices is in an open field, there are many threats Security researchers involved this topic ensure safety system adversary can initiate cyber attacks, such DDoS attacks making a service unavailable then injecting false data tell us that equipment safe but reality, it has been theft. In paper, we propose deep learning-based intrusion detection on three models, namely, convolutional neural networks, recurrent networks. Each model’s performance studied within two classification types (binary multiclass) using real traffic datasets, CIC-DDoS2019 dataset TON_IoT dataset, which contain different attacks.

Язык: Английский

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Review of the internet of things communication technologies in smart agriculture and challenges

Computers and Electronics in Agriculture, Год журнала: 2021, Номер 189, С. 106352 - 106352

Опубликована: Авг. 14, 2021

Язык: Английский

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Applications of machine vision in agricultural robot navigation: A review

Computers and Electronics in Agriculture, Год журнала: 2022, Номер 198, С. 107085 - 107085

Опубликована: Июнь 1, 2022

Язык: Английский

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Augmented reality in vocational training: A systematic review of research and applications

Computers in Human Behavior, Год журнала: 2021, Номер 129, С. 107125 - 107125

Опубликована: Дек. 15, 2021

Язык: Английский

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A Deep Learning-Based Model for Date Fruit Classification

Sustainability, Год журнала: 2022, Номер 14(10), С. 6339 - 6339

Опубликована: Май 23, 2022

A total of 8.46 million tons date fruit are produced annually around the world. The is considered a high-valued confectionery and crop. hot arid zones Southwest Asia, North Africa, Middle East major producers fruit. production dates in 1961 was 1.8 tons, which increased to 2.8 1985. In 2001, recorded at 5.4 whereas recently it has reached tons. common problem found industry absence an autonomous system for classification fruit, resulting reliance on only manual expertise, often involving hard work, expense, bias. Recently, Machine Learning (ML) techniques have been employed such areas agriculture farming brought great convenience human life. An automated based ML can carry out sorting tasks that were previously handled by experts. various fields, CNNs (convolutional neural networks) achieved impressive results image classification. Considering success transfer learning other problems, this research also employs similar approach proposes efficient model. research, dataset eight different classes created train proposed Different preprocessing applied model, as augmentation, decayed rate, model checkpointing, hybrid weight adjustment increase accuracy rate. show MobileNetV2 architecture 99% accuracy. compared with existing models AlexNet, VGG16, InceptionV3, ResNet, MobileNetV2. prove performs better than all terms

Язык: Английский

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