Online farming based on embedded systems and wireless sensor networks

Kannan, Kamala; Thilagavathi, G.

doi:10.1109/iccpeic.2013.6778501

Cited by 15 publications

(5 citation statements)

References 7 publications

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“…In general, most of the sensor nodes used to monitor soil data (such as temperature, humidity, and matric potential) and weather data (such as temperature and relative humidity) are operated using open, low-cost hardware platforms such as the Arduino [10][11][12][13][14] or Raspberry Pi [15][16][17]. Zigbee [11,[17][18][19], LoRa [16], Wifi [20], Bluetooth [14], GSM [12], and GPRS [21] are the predominant wireless technologies used.…”

Section: Data Gatheringmentioning

confidence: 99%

Smart & Green: An Internet-of-Things Framework for Smart Irrigation

Campos

Rocha²,

Gondim

et al. 2019

Sensors

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Irrigation is one of the most water-intensive agricultural activities in the world, which has been increasing over time. Choosing an optimal irrigation management plan depends on having available data in the monitoring field. A smart agriculture system gathers data from several sources; however, the data are not guaranteed to be free of discrepant values (i.e., outliers), which can damage the precision of irrigation management. Furthermore, data from different sources must fit into the same temporal window required for irrigation management and the data preprocessing must be dynamic and automatic to benefit users of the irrigation management plan. In this paper, we propose the Smart&Green framework to offer services for smart irrigation, such as data monitoring, preprocessing, fusion, synchronization, storage, and irrigation management enriched by the prediction of soil moisture. Outlier removal techniques allow for more precise irrigation management. For fields without soil moisture sensors, the prediction model estimates the matric potential using weather, crop, and irrigation information. We apply the predicted matric potential approach to the Van Genutchen model to determine the moisture used in an irrigation management scheme. We can save, on average, between 56.4% and 90% of the irrigation water needed by applying the Zscore, MZscore and Chauvenet outlier removal techniques to the predicted data.

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Section: Data Gatheringmentioning

confidence: 99%

Smart & Green: An Internet-of-Things Framework for Smart Irrigation

Campos

Rocha²,

Gondim

et al. 2019

Sensors

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“…In this, a sensing mechanism is utilized to control productive chain, nodes that are installed in the field, thus the gathered information recollects the quality of grapes. In [15,17], the authors discussed security issues in IoT and the designed system architecture is well elaborated with its security issues present in 3-layer architectures. They also explained key management and its corresponding access control mechanisms.…”

Section: Fig3 End-to-end Systemmentioning

confidence: 99%

An Efficient IoT Based Smart Irrigation System and Plant Diseases Detection: A Review

NagaGeetha¹,

Ramesh²

2018

IJET

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The irrigation control and monitoring of the involved variables on the plant growth are actions that farmers must implement in their crops to save water resources and assessing the growth of the plant. In this paper, the literature is conducted on several authors’ works, their modern way of doing agriculture with IoT and image processing combination, thus the paper gives information about various techniques, algorithms, and architectures employed to detect the crop diseases in advance so that proper care can be taken to avoid it. Hence incorporating IoT with image processing gives improved detection ability in the agricultural system. The survey comprises several authors’ workouts of automated Irrigation and crop diseases Detection practices which are currently in use..

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“…Based on the research survey, it is understood that the agricultural sector is heavily involved in employing IoT sensors to collect data, which later helps in data mining and predicting using various artificial intelligence (AI) techniques. However, in recent times, low-cost data gathering electronic devices are becoming popular which include Arduino [18][19][20][21][22], Raspberry Pi [23][24][25], Zigbee [19,[25][26][27], LoRa [24], WiFi [28], Bluetooth [22], Global System for Mobile (GSM) Communications [20], and General Packet Radio Service (GPRS) [29]. As well, there has been a rapid influx of commercial water sprinkling and pest spraying farming solutions embedded with above mentioned electronic devices into the market.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Photovoltaic Powered Battery-Operated Computer Vision-Based Multi-Purpose Smart Farming Robot

et al. 2021

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Farm machinery like water sprinklers (WS) and pesticide sprayers (PS) are becoming quite popular in the agricultural sector. The WS and PS are two distinct types of machinery, mostly powered using conventional energy sources. In recent times, the battery and solar-powered WS and PS have also emerged. With the current WS and PS, the main drawback is the lack of intelligence on water and pesticide use decisions and autonomous control. This paper proposes a novel multi-purpose smart farming robot (MpSFR) that handles both water sprinkling and pesticide spraying. The MpSFR is a photovoltaic (PV) powered battery-operated internet of things (IoT) and computer vision (CV) based robot that helps in automating the watering and spraying process. Firstly, the PV-powered battery-operated autonomous MpSFR equipped with a storage tank for water and pesticide drove with a programmed pumping device is engineered. The sprinkling and spraying mechanisms are made fully automatic with a programmed pattern that utilizes IoT sensors and CV to continuously monitor the soil moisture and the plant’s health based on pests. Two servo motors accomplish the horizontal and vertical orientation of the spraying nozzle. We provided an option to remotely switch the sprayer to spray either water or pesticide using an infrared device, i.e., within a 5-m range. Secondly, the operation of the developed MpSFR is experimentally verified in the test farm. The field test’s observed results include the solar power profile, battery charging, and discharging conditions. The results show that the MpSFR operates effectively, and decisions on water use and pesticide are automated.

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Online farming based on embedded systems and wireless sensor networks

Cited by 15 publications

References 7 publications

Smart & Green: An Internet-of-Things Framework for Smart Irrigation

Smart & Green: An Internet-of-Things Framework for Smart Irrigation

An Efficient IoT Based Smart Irrigation System and Plant Diseases Detection: A Review

Design and Analysis of Photovoltaic Powered Battery-Operated Computer Vision-Based Multi-Purpose Smart Farming Robot

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