Review of operational management in intelligent agriculture based on the Internet of Things

Hu, Xiangpei; Sun, Lixian; Zhou, Yaxian; Ruan, Junhu

doi:10.1007/s42524-020-0107-3

Cited by 57 publications

(32 citation statements)

References 15 publications

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“…These new and appropriate technologies can provide useful information that aids in reducing the water crisis [6], [11]. WSNs have a wide range of applications, including industrial [21], [22] smart cities [23], [24], traffic control [25], [26], healthcare [27], [28], agriculture [29], [30], and environmental monitoring [31], [32]. Self-powered WSNs deployed in remote areas and that provide data in a near real-time manner can potentially revolutionize groundwater table management.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of a Low-Cost, Low- Power, LoRa-GSM, IoT Enabled System for Monitoring of Groundwater Resources With Energy Harvesting Integration

Kombo¹,

Kumaran

Bovim³

2021

IEEE Access

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The rapid expansion of Internet of Things (IoT) devices and applications has accelerated research in various areas of human development. However, the cost of commercial instrumentation impedes the momentum of technological growth in developing regions. In this study, a low-cost, low-power, wireless sensor network for groundwater monitoring (LWNGM) was developed to provide near real-time groundwater level data to support prudent decision making in groundwater resource management in Zanzibar, Tanzania. To facilitate reproducibility, we provided a detailed description of the LWNGM development procedure. The system is based on the ATmega328P microcontroller platform and incorporates low-cost MS5803-14BA and MB280 sensors. To provide a low-power scheme, the Arduino UNO wakes up in six-hour intervals for measurements and data-logging to the SD card, and at twelve-hour intervals for relaying data (in batches) to the LoRa gateway, before it goes back into a deep-sleep mode for the rest of the time (duty cycle<1% ). The average power consumption for the end node across all system cycles was 104.081mW. The power autonomy of all nodes is provided by a 3.7V, 5000mAh rechargeable LiPo battery, and a 9V, 600mAh rechargeable Liion battery, respectively, which are supported by 6V and, 3W solar chargers. The data processing and storage components, as well as the data visualization dashboard, were created using free and open-source software. The LWNGM was reasonably priced, ranging between $350 and $400. The proposed system allows for the adoption of hydrological monitoring, particularly in areas with a limited budget for hydrologic management.

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Section: Introductionmentioning

confidence: 99%

Design and Application of a Low-Cost, Low- Power, LoRa-GSM, IoT Enabled System for Monitoring of Groundwater Resources With Energy Harvesting Integration

Kombo¹,

Kumaran

Bovim³

2021

IEEE Access

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“…Principles of Sensor Technology Based on a Sensory Feedback Mechanism. The ranking algorithms for sensors with sensory feedback mechanisms present in IoT are CAS-SARAM in which the user selects the sensor attributes and inputs the weights of each sensor attribute to ensure that their sum is 1, and then, the Euclidean distance algorithm is used for the ranking calculation, which is ranked according to the distance; the algorithm is simple but loses some accuracy; in literature [16], AHP is used for the weighting of relatively several sensor attributes for normalized calculation of weighted scores for dependencies among sensor nodes, etc., in the underlying IoT and ranking according to the size of the scores; literature [17] proposed a similarity comparison ranking algorithm to compare the similarity of the output data of a given sensor over some time with the user search output data for ranking. The literature [18] proposed a prediction using statistical data from the past period, and the search and prediction model helps to find the matching sensors retrieved with the minimum number of sensor data.…”

Section: Application Of Sensor Technologymentioning

confidence: 99%

Perceptual Feedback Mechanism Sensor Technology in e‐Commerce IoT Application Research

Guo

Liang

2021

Journal of Sensors

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With the development of sensor technology and the Internet of Things (IoT) technology, the trend of miniaturization of sensors has prompted the inclusion of more sensors in IoT, and the perceptual feedback mechanism among these sensors has become particularly important, thus promoting the development of multiple sensor data fusion technologies. This paper deeply analyzes and summarizes the characteristics of sensory data and the new problems faced by the processing of sensory data under the new trend of IoT, deeply studies the acquisition, storage, and query of sensory data from the sensors of IoT in e-commerce, and proposes a ubiquitous storage method for massive sensory data by combining the sensory feedback mechanism of sensors, which makes full use of the storage resources of IoT storage network elements and maximally meets the massive. In this paper, we propose a ubiquitous storage method for massive sensing data, which makes full use of the storage resources of IoT storage network elements to maximize the storage requirements of massive sensing data and achieve load-balanced data storage. In this paper, starting from the overall development of IoT in recent years, the weak link of intelligent information processing is reinforced based on the sensory feedback mechanism of sensor technology.

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“…According to the mechanical industry standard experimental method in the People's Republic of China Transplanter of dry land plant (JB/T 10291-2013), the transplanting qualified rate of the transplanter required is ≥90%, which means up to 10% of the land could be wasted, and there is also waste of fertilizers, pesticides, and water. During the transplanting process, the growth status of plug seedlings is detected in real time, and the unqualified seedlings (dead, weak, diseased, and injured seedlings) are eliminated, and only robust seedlings are planted, which can improve land utilization, reduce waste of resources, and meet the requirements of precision, automatic, and intelligent agriculture [6,7]. The research on seedling selection systems has important significance for automatic transplanting and further development of the vegetable industry [8,9].…”

Section: Introductionmentioning

confidence: 99%

Design of and Experiment with Seedling Selection System for Automatic Transplanter for Vegetable Plug Seedlings

et al. 2021

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In the process of vegetable plug seedling cultivation, packaging, and transportation, there may be missing, unhealthy or injured seedlings in the tray, which results in a missed planting or a low seedling survival rate after automatic transplanting. In this study, a seedling selection system with the function of seedlings identification, week seedlings elimination, and missing seedlings supplement was developed for an automatic transplanter. A plug seedling identification system based on a machine vision was used to detect vegetable plug seedlings based on the area characteristics of plug seedlings, stem leaves and plug bodies. The identification results were transmitted to a programmable logic controller (PLC), which controlled a nozzle to eliminate the unqualified seedlings from the conveyor belt lattice. When the empty conveyor belt lattice reaches the seedling throwing funnel, the rear conveyor belt lattice with the plug seedling is accelerated to ensure the continuity of seedlings supply. The adaptive fuzzy PID control algorithm was used to control the stepper motor of the conveyor belt to realize accurate seedling conveying and a seedling supplement. Using 30 days pepper plug seedlings as experimental seedlings, a comparative field experiment was carried out to evaluate the performance of the seedling selection system. The results showed that when the seedling selection system was turned on and the seedling extracting frequencies were 60, 80, and 100 plants/min, the success rates of plug seedling identification were 98.84%, 98.38%, and 96.99%, and the robust seedling rates were 98.05%, 97.78%, and 95.83%. The robust seedling rates were increased by 15.64%, 16.07%, and 13.89%, respectively, in contrast to turning off the seedling selection system.

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Review of operational management in intelligent agriculture based on the Internet of Things

Cited by 57 publications

References 15 publications

Design and Application of a Low-Cost, Low- Power, LoRa-GSM, IoT Enabled System for Monitoring of Groundwater Resources With Energy Harvesting Integration

Design and Application of a Low-Cost, Low- Power, LoRa-GSM, IoT Enabled System for Monitoring of Groundwater Resources With Energy Harvesting Integration

Perceptual Feedback Mechanism Sensor Technology in e‐Commerce IoT Application Research

Design of and Experiment with Seedling Selection System for Automatic Transplanter for Vegetable Plug Seedlings

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