IoT Sensing Platform as a Driver for Digital Farming in Rural Africa

Oliveira-Jr, Antônio; Resende, Carlos; Pereira, André Alves; Madureira, Pedro; Gonçalves, João; Moutinho, R.; Soares, Filipe; Moreira, Waldir

doi:10.3390/s20123511

Cited by 30 publications

(12 citation statements)

References 26 publications

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“…A number of various IoT supported hardware platforms exist to be used within the agricultural domains. Table 4 categorizes current and legacy platforms based on main criteria [104,156,190,191].…”

Section: Iot Based Smart Farming Sensor Systemsmentioning

confidence: 99%

State of the Art of Urban Smart Vertical Farming Automation System: Advanced Topologies, Issues and Recommendations

2021

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The global economy is now under threat due to the ongoing domestic and international lockdown for COVID-19. Many have already lost their jobs, and businesses have been unstable in the Corona era. Apart from educational institutions, banks, privately owned institutions, and agriculture, there are signs of economic recession in almost all sectors. The roles of modern technology, the Internet of things, and artificial intelligence are undeniable in helping the world achieve economic prosperity in the post-COVID-19 economic downturn. Food production must increase by 60% by 2050 to meet global food security demands in the face of uncertainty such as the COVID-19 pandemic and a growing population. Given COVID 19’s intensity and isolation, improving food production and distribution systems is critical to combating hunger and addressing the double burden of malnutrition. As the world’s population is growing day by day, according to an estimation world’s population reaches 9.6 billion by 2050, so there is a growing need to modify the agriculture methods, technologies so that maximum crops can be attained and human effort can be reduced. The urban smart vertical farming (USVF) is a solution to secure food production, which can be introduced at any adaptive reuse, retrofit, or new buildings in vertical manners. This paper aims to provide a comprehensive review of the concept of USVF using various techniques to enhance productivity as well as its types, topologies, technologies, control systems, social acceptance, and benefits. This review has focused on numerous issues, challenges, and recommendations in the development of the system, vertical farming management, and modern technologies approach.

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Section: Iot Based Smart Farming Sensor Systemsmentioning

confidence: 99%

State of the Art of Urban Smart Vertical Farming Automation System: Advanced Topologies, Issues and Recommendations

2021

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“…The application of IoT networks [13] is increasingly becoming part of the people lives, carrying out the control of complex systems [14], environmental monitoring [15], precision agriculture [16,17], digital farming [18], digital health [19], and smart homes. Several researches have employed IoT networks to collect big data for analysis and complex decision making.…”

Section: Related Workmentioning

confidence: 99%

A Standard-Based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring

Filho¹,

Fernando

Rabelo

et al. 2021

Sensors

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The environment consists of the interaction between the physical, biotic, and anthropic means. As this interaction is dynamic, environmental characteristics tend to change naturally over time, requiring continuous monitoring. In this scenario, the internet of things (IoT), together with traditional sensor networks, allows for the monitoring of various environmental aspects such as air, water, atmospheric, and soil conditions, and sending data to different users and remote applications. This paper proposes a Standard-based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring. The platform consists of an IoT network based on the IEEE 1451 standard which has the network capable application processor (NCAP) node (coordinator) and multiple wireless transducers interface module (WTIM) nodes. A WTIM node consists of one or more transducers, a data transfer interface and a processing unit. Thus, with the developed network, it is possible to collect environmental data at different points within a city landscape, to perform analysis of the communication distance between the WTIM nodes, and monitor the number of bytes transferred according to each network node. In addition, a dynamic model of data flow is proposed where the performance of the NCAP and WTIM nodes are described through state variables, relating directly to the information exchange dynamics between the communicating nodes in the mesh network. The modeling results showed stability in the network. Such stability means that the network has capacity of preserve its flow of information, for a long period of time, without loss frames or packets due to congestion.

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“…Disease identification tasks use Deep Neural Networks, and several applications have been developed to improve the detection and correctness of the disease on the plantation. In addition, thermal cameras to detect the humidity and prediction of disease on leaves [ 6 ], classification of soil conditions [ 23 ], and automatic irrigation of the plantation [ 24 ] already were proposed.…”

Section: Introductionmentioning

confidence: 99%

Smart Strawberry Farming Using Edge Computing and IoT

Cruz

Mafra

Teixeira

et al. 2022

Sensors

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Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. This model supports efficient disease detection with 92% accuracy. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed.

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IoT Sensing Platform as a Driver for Digital Farming in Rural Africa

Cited by 30 publications

References 26 publications

State of the Art of Urban Smart Vertical Farming Automation System: Advanced Topologies, Issues and Recommendations

State of the Art of Urban Smart Vertical Farming Automation System: Advanced Topologies, Issues and Recommendations

A Standard-Based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring

Smart Strawberry Farming Using Edge Computing and IoT

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