An Intelligent Irrigation Scheduling System Using Low-Cost Wireless Sensor Network Toward Sustainable and Precision Agriculture

Jamroen, Chaowanan; Komkum, Preecha; Fongkerd, Chanon; Krongpha, Wipa

doi:10.1109/access.2020.3025590

Cited by 94 publications

(52 citation statements)

References 45 publications

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“…The integrated control system identifies the maximum efficiency point and the minimum energy consumption in the entire irrigation system using a decision matrix which takes into account the pumping cost, the pumping group efficiency and the total flow required at a constant pressure. This is in contrast to previous studies that consider soil moisture, [9], air temperature and solar radiation, [10] irrigation water withdrawal in [11] and pump speed and flow in [12].…”

Section: Introductioncontrasting

confidence: 68%

A Hybrid Methodology Based on Smart Management Energy Consumption in Irrigation Systems

et al. 2021

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Innovative practices in irrigation systems can bring improvements in terms of economic efficiency and, at the same time, can reduce environmental impacts. Investment in high-tech technologies frequently involves additional costs, but an efficient water management system can increase the lifetime of the equipment. The most utilized electronic device for a smart management, used to pump units from irrigation systems, is the frequency converter. This device can regulate the speed of the motors that control the pumps according to the consumption of water, ensuring that it does not pump more water than is needed. This paper develops a new operating algorithm that ensures the operation of the pumping group at safe operating intervals and identifies the equivalent pump operating points for the entire flow range and pumping height of the pumping group in order to bring smart management to irrigation systems. The parameters monitored and collected for each vertical pump refers to the voltage, current, frequency (speeds) and flow of each hydraulic operating mode. The methodology used is based on the principle of creating an expert system to optimize energy consumption in the pumping groups. The proposed methodology was tested on an irrigation system that includes a pumping group with five pumps, showing its effectiveness in obtaining the optimal solution with a relatively low computational burden and without the violation of any system constraints under any operating conditions.

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Section: Introductioncontrasting

confidence: 68%

A Hybrid Methodology Based on Smart Management Energy Consumption in Irrigation Systems

et al. 2021

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“…The current study of WSN is also focused on incorporating intelligence in the process of irrigation in PA. The work carried out by Jamroen et al [85] has developed an irrigation scheduling mechanism using fuzzy logic in WSN. The outcome witnessed an increase in crop yield.…”

Section: Wsn In Precision Agriculturementioning

confidence: 99%

Emerging Line of Research Approach in Precision Agriculture: An Insight Study

Vanishree¹,

Nagaraja²

2021

IJACSA

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The present state of agriculture and its demand is very much different than what it used to be two decades back. Hence, Precision Agriculture (PA) is more in demand to address this challenging demand. With consistent pressure to develop multiple products over the same agricultural land, farmers find PA's adoption the best rescue-based solution with restricted resources. PA accelerates the yield and potentially assists in catering up the demand of scarcity of demands of food. With the increasing adoption of PA-based technologies over farming, there are best possibilities to explore efficient farming practices and better decision-making facilitated by real-time data availability. There isan evolution of various novel technologies to boost agricultural performance, i.e. variable rate technology, Geomapping, remote sensing, automated steering system, and satellite positioning system. Apart from this, it is also observed that Internet-of-Things (IoT) and Wireless Sensor Network (WSN) have been slowly penetrating this area to acceleratePA's technological advancement. It is noticed that the adoption of sensing technology is a common factor in almost all the techniques used in PA. However, there is no clear idea about the most dominant approach in this regard. Therefore, this paper discusses existing approaches concerning standard conventional PA and sensing-based PA using WSN. The study contributes towards some impressive learning outcomes to state that WSN and IoT are extensive to boost PA.

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“…In recent years, studies on wireless sensor networks (WSNs) have demonstrated their effectiveness for many remote sensing applications including precision agriculture [1][2][3], logistics carrier monitoring [4], industrial monitoring and control [5], underground coal mines monitoring [6,7], monitoring forest fire [8,9], monitoring seismic activity [10], marine environment monitoring [11], etc. The major advantage of WSNs technology is that it is ad-hoc and easy to manage and configure.…”

Section: Introductionmentioning

confidence: 99%

Accurate Location Estimation of Smart Dusts Using Machine Learning

Bashir¹,

Malik²,

Lai³

2022

Computers, Materials &Amp; Continua

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Traditional wireless sensor networks (WSNs) are not suitable for rough terrains that are difficult or impossible to access by humans. Smart dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough terrains. The tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraft without manually setting their locations. Although it is clear that a number of remote sensing applications can benefit from this technology, but the small size of smart dust fundamentally restricts the integration of advanced hardware on tiny sensors. This raises many challenges including how to estimate the location of events sensed by the smart dusts. Existing solutions on estimating the location of events sensed by the smart dusts are not suitable for monitoring rough terrains as these solutions depend on relay sensors and laser patterns which have their own limitations in terms of power constraint and uneven surfaces. The study proposes a novel machine learning based localization algorithm for estimating the location of events. The approach utilizes timestamps (time of arrival) of sensed events received at base stations by assembling them into a multidimensional vector and input to a machine learning classifier for estimating the location. Due to the unavailability of real smart dusts, we built a simulator for analysing the accuracy of the proposed approach for monitoring forest fire. The experiments on the simulator show reasonable accuracy of the approach.

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An Intelligent Irrigation Scheduling System Using Low-Cost Wireless Sensor Network Toward Sustainable and Precision Agriculture

Cited by 94 publications

References 45 publications

A Hybrid Methodology Based on Smart Management Energy Consumption in Irrigation Systems

A Hybrid Methodology Based on Smart Management Energy Consumption in Irrigation Systems

Emerging Line of Research Approach in Precision Agriculture: An Insight Study

Accurate Location Estimation of Smart Dusts Using Machine Learning

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