Development of a smart mobile farming service system

Zheng, Lihua; Li, Minzan; Wu, Caicong; Ye, Haijian; Ji, Ronghua; Deng, Xiaolei; Che, Yanshuang; Fu, Cheng; Guo, Wei

doi:10.1016/j.mcm.2010.11.053

Cited by 40 publications

(14 citation statements)

References 4 publications

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“…Digital farming is one of the key technologies which has been gaining support through different research initiatives in the last decade. The fundamental digital farming setup consists of the central management platform [1], human-computer interfaces [2] and particular IoT modules [3], which monitor the animals, their behavior and facilities. All these building blocks are connected [4] into a common platform, using communication networks which are, today, frequently based on IoT architecture combined with cloud or edge-based functionalities.…”

Section: Introductionmentioning

confidence: 99%

Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service

Žgank

2019

Sensors

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Beekeeping is one of the widespread and traditional fields in agriculture, where Internet of Things (IoT)-based solutions and machine learning approaches can ease and improve beehive management significantly. A particularly important activity is bee swarming. A beehive monitoring system can be applied for digital farming to alert the user via a service about the beginning of swarming, which requires a response. An IoT-based bee activity acoustic classification system is proposed in this paper. The audio data needed for acoustic training was collected from the Open Source Beehives Project. The input audio signal was converted into feature vectors, using the Mel-Frequency Cepstral Coefficients (with cepstral mean normalization) and Linear Predictive Coding. The influence of the acoustic background noise and denoising procedure was evaluated in an additional step. Different Hidden Markov Models' and Gaussian Mixture Models' topologies were developed for acoustic modeling, with the objective being to determine the most suitable one for the proposed IoT-based solution. The evaluation was carried out with a separate test set, in order to successfully classify sound between the normal and swarming conditions in a beehive. The evaluation results showed that good acoustic classification performance can be achieved with the proposed system.In the case of mobile networks, the 5G, and the already existing LTE-M1/NB-IoT, enable ubiquitous connectivity, which is important when the system gets distributed over more than one location. The increased number of connected devices, many of them originating from wireless-distributed sensor networks, were also responsible for the last major changes in network protocol standardization, where IPv6 was defined and deployed to communication networks, with the objective to cope with the new situation.If IoT and intelligent ambiance present one of the upfront sectors, agriculture is still a more traditional one. ICT technologies were introduced widely [6] to agriculture in the last two decades, but there is a large area of possibilities on how, additionally, to include real-time monitoring, machine learning and sensor networks into this sector. Sensor networks enable numerous solutions for smart farming [7], intending to increase the production rate and improve sustainability, but they are also, in parallel, in charge of taking care of animal condition and health. Beekeeping is an important part of agriculture, where the distributed locations are frequently present, and thus, indicate the need to ease the monitoring of animals in a 24/7 mode, which can benefit from advanced intelligent ambiance technologies. The popularity of modern urban beekeeping initiatives has gained further momentum to this research field, and presents an additional possibility as to how to include digital agriculture into a smart city and smart home solutions [8].Swarming has always been seen by beekeepers as an extremely important event, which requires an immediate response. Beekeepers were used to observing the beeh...

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Section: Introductionmentioning

confidence: 99%

Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service

Žgank

2019

Sensors

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“…Perception layer is composed of various sensors and a monitoring system. The transport layer, which is composed of WLAN, 3G, and WIFI and so on, transmits the data obtained from the perception layer [11]. Processing layer is composed of two parts: hardware and software, the hardware includes the storage server and computing server, software mainly is the information management system, responsible for the transport layer data analysis, processing and handling, and the results through the platform for publishing.…”

Section: Smart Agriculture Demonstration Park Service Composition Strmentioning

confidence: 99%

Study on the Construction of Smart Agricultural Demonstration Park

Zhang¹,

Zhou²,

Li³

2014

IJSH

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“…To increase the production and reduce man power efforts in agriculture, researchers started applying various techniques in farming. Technologies such as sensor network, 2‐4 Global Positioning System (GPS), 5,6 and remote sensing 7,8 are used in agriculture to automate farming technique. The major issues with these technologies are farmers/end users cannot access or operate necessary functions from remote places, interoperability, complex structure, difficulties in adding new devices, cost, high energy conservation, and so on.…”

Section: Introductionmentioning

confidence: 99%

Systematic review of Internet of Things in smart farming

Terence

Geethanjali

2020

Trans Emerging Tel Tech

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Agriculture unquestionably is one of the traditional occupations, which feeds all mankind in the world. Continuous changes are happening in the agricultural field to increase production. Researchers are applying various techniques to improve farming methods. To monitor plants even from remote places and to improve the yield of plants, Internet of Things (IoT), which is a boon in today's world, is applied in farming, in general, known as smart farming. Smart farming is a way where the farmers can monitor their field and manage farming activities from remote places. This reduces man power and increases resource utilization in farming. In this article, we have studied the architecture of smart farming and studied different smart farming techniques, also we have classified smart farming techniques into three categories, namely, IoT-based agricultural monitoring and controlling system, automatic irrigation system, and plant disease monitoring system. The review for the article is selected based on the systematic literature review method, and articles published from 2011 to 2019 are considered for review. Different IoT technologies such as sensors, gateway, communication system, user interface and experiment nature, plant type, disease type, advantages, and limitations are also reviewed. Future research direction and challenges in smart farming techniques are also discussed. Trans Emerging Tel Tech. 2020;31:e3958. wileyonlinelibrary.com/journal/ett Atzori et al 16 presented a survey on IoT techniques. In this article, the architecture, applications, and challenges of IoT technologies were discussed. Various environmental and agricultural monitoring applications were discussed. Zhang et al 17 projected various agricultural data transmission methods in his review article, namely, voice information transmission, short message service (SMS) information transmission, online information transmission, video conference information transmission, and multichannel-based information transmission techniques. Case studies, features, applications, examples, and limitations of various information transmission techniques were discussed. Kamilaris et al 18 projected survey on big data analysis in agriculture. In this article, 34 agriculture works were considered for the study and different features such as tools applied, big data algorithm used, problem and proposed solution implemented, and data used were discussed. The type of plants and experimental duration and advantages and limitation of algorithms were missing. Talavera et al 14 reviewed IoT applications in environment and agroindustrial system. The authors discussed about various IoT techniques involved in monitoring, communication, logistics, energy and resource management in environment monitoring, and agroindustrial system. In this study, the authors approached all the IoT techniques with following two questions: what is the primary technical solution of IoT in environmental monitoring and agroindustrial system and which methodology and IoT components such as sensors, actuato...

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Development of a smart mobile farming service system

Cited by 40 publications

References 4 publications

Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service

Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service

Study on the Construction of Smart Agricultural Demonstration Park

Systematic review of Internet of Things in smart farming

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