Smart Irrigation System Using Internet of Things

Leh, Nor Adni Mat; Kamaldin, Muhammad Syazwan Ariffuddin Mohd; Muhammad, Zuraida; Kamarzaman, Nur Atharah

doi:10.1109/icsengt.2019.8906497

Cited by 49 publications

(11 citation statements)

References 3 publications

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“…Fraga-Lamas et al [40] proposed IoT-assisted smart irrigation by exploiting the LoRaWAN-based architecture to avoid the unavailability of the Internet in remote areas. Leh et al proposed [41] a soil moisture-based smart irrigation water system by assessment of soil moisture conditions for long-distance communication with reduced power consumption. Shi et al [42] suggested a method to determine the Plant Water Deficit Index (PWDI) based on soil moisture conditions and recommend irrigation water accordingly.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT

Bashir

Rehman

et al. 2022

IEEE Access

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Accurate estimation of Reference Evapotranspiration (ET 0 ) is important for efficient management and conservation of irrigation water. Existing methods of ET 0 rate determination are complex for application at the farmer level. Apart from standard methods of ET 0 determination, many data-driven soft computing approaches were also proposed to determine the ET 0 with limited data set. We proposed a temperature and humidity-based ML approach for ET 0 rate determination on directly sensed environmental conditions of the crop field. Crop field environmental conditions for (ET 0 ) rate determination are sensed by the proposed Internet of Things (IoT) architecture. Crop field environmental conditions from the year 2015 to 2021 in Pakistan are used for the training and testing of the proposed model. Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Artificial Neural Network (ANN) based models are compared for performance. Crop fields directly sensed temperature and humidity pass to the model to train and predict the ET 0 -rate of crop fields. The 10-fold cross-validation technique is applied for the evaluation of the proposed approach. The accuracy of the proposed solution for the ET 0 rate is compared against the Food and Agriculture Organization (FAO) recommended Penman-Monteith method for ET 0 rate determination. As concerned of the ML-based models the KNN model is more accurate as compared to SVM,GNB and ANN models with 92% accuracy. The KNN model of ET 0 is more efficient in reducing the Root Mean Squared Errors (RMSE) by 16% and Mean Absolute Errors (MAE) by 3% against the state of the art approach.INDEX TERMS Internet of Things (IoT), Reference Evapotranspiration (ET 0 ), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), K-Nearest Neighbour (KNN), Artificial Neural Network (ANN), Penman-Monteith

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Section: Related Workmentioning

confidence: 99%

Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT

Bashir

Rehman

et al. 2022

IEEE Access

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“…Its output sensitivity ranges from 66 to 185 mV/ A. A hall effect current sensor called the ACS712 is used to measure current [29]. It is both cost-effective and provides a precise solution for AC and DC current detection.…”

Section: Current Sensormentioning

confidence: 99%

Monitoring of Single-Phase Induction Motor through IoT Using ESP32 Module

Shukla

Chacko³

et al. 2022

Journal of Sensors

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The condition monitoring of rotating machines for critical applications plays an important role in reducing downtime. With Industry 4.0, the role of IoT in online condition monitoring of electrical machines has gained considerable significance. The main aim of the paper is the use of IoT for online monitoring of motor parameters like current, temperature, vibration, and humidity and observing its online trending using a web server. Data can be accessed in form of graphs and widgets by visiting the web page. The advantage of this project is the real-time monitoring of the motor from any remote area and in case of any abnormality operating personnel can take necessary steps for preventing complete breakdown. The proposed work can help industry people in online monitoring of motors and in the future work can be extended for fault prediction and classification.

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“…Also, Leh et al [20] designed a smart irrigation system, the system is split into two parts: hardware and software. Three components make up the hardware: a soil moisture sensor, a temperature and humidity sensor (DHT 11), and an ESP 8266 Wi-Fi module connected to an Arduino Mega 2560 microprocessor.…”

Section: Related Workmentioning

confidence: 99%

An internet of things-based irrigation and tank monitoring system

Obasanya

Oluwafemi

Bello

et al. 2022

IJ-ICT

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<span>Agriculture plays a significant role in the development of a nation and provides the main source of food production, income, and employment to nations. It was the most practiced occupation in Nigeria and this formed the backbone of the economy in the early 1960s before the discovery of crude oil, which has led to the derail of sufficient food production, exportation, and the agricultural economy at large. Over time, the dry season has always been challenging with little or no rainfall and there are no irrigation facilities that incorporate different saving practices to adapt to these climate changes on their own. In this paper, a cost-effective internet of things irrigation system that is capable of reducing water wastage, manual labor, monitoring tank water level and that can be controlled remotely is designed. The system integrated Arduino UNO with a soil moisture sensor, HCSR04 ultrasonic sensor, and ESP8266 Wi-Fi module that gives the system capable of being controlled remotely via the internet, thus achieving optimal irrigation using the internet of things (IoT). Some of the challenges facing the existing irrigation system are water wastage, poor performance, and high cost of implementation. The design system helps to control water supply to crops when it is needed, and also monitors soil moisture, temperature, and water tank level. After carrying out the experiments for 15 days, the system saved approximately 49% of the water used in traditional irrigation method. The system is useful in large farming areas to minimize human effort and reduce the cost of hiring personnel.</span>

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Smart Irrigation System Using Internet of Things

Cited by 49 publications

References 3 publications

Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT

Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT

Monitoring of Single-Phase Induction Motor through IoT Using ESP32 Module

An internet of things-based irrigation and tank monitoring system

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Smart Irrigation System Using Internet of Things

Cited by 49 publications

References 3 publications

Machine Learning Based Prediction of Reference Evapotranspiration (ET0) Using IoT

Machine Learning Based Prediction of Reference Evapotranspiration (ET0) Using IoT

Monitoring of Single-Phase Induction Motor through IoT Using ESP32 Module

An internet of things-based irrigation and tank monitoring system

Contact Info

Product

Resources

About

Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT

Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT