Low-Cost Wireless Monitoring and Decision Support for Water Saving in Agriculture

Viani, Federico; Bertolli, M.; Salucci, Marco; Polo, Alessandro

doi:10.1109/jsen.2017.2705043

Cited by 130 publications

(58 citation statements)

References 31 publications

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“…In the works [16] and [17], systems were proposed for agricultural monitoring. In [16], the designed system used WiFi-based IoT devices to monitor nitrate con-centrations in ground water.…”

Section: Related Workmentioning

confidence: 99%

Wireless technologies for smart agricultural monitoring using internet of things devices with energy harvesting capabilities

Sadowski

Spachos

2020

Computers and Electronics in Agriculture

111

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“…In the works [16] and [17], systems were proposed for agricultural monitoring. In [16], the designed system used WiFi-based IoT devices to monitor nitrate con-centrations in ground water.…”

Section: Related Workmentioning

confidence: 99%

Wireless technologies for smart agricultural monitoring using internet of things devices with energy harvesting capabilities

Sadowski

Spachos

2020

Computers and Electronics in Agriculture

111

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“…In Viani et al, 25 fuzzy logic-based DSS system is developed using the WSN for smart irrigation. Dramatic advances in RFIC design opened up the application of WSN for a variety of agricultural applications.…”

Section: Related Workmentioning

confidence: 99%

“…6,9,25,31,41 The values considered for constants X,Y and Z in Miao et al 20 may not be applicable in the context of the variation in environment size or variation in physical obstacles present in the farmland. It can be seen that unlike LNSM model, there is no term (variable) in the OFPEDM model to account for any uncertainty in the measurement noise.…”

Section: Fundamentals Of Lnsm and Ofpedm Path Loss Modelsmentioning

confidence: 99%

“…Dramatic advances in RFIC design opened up the application of WSN for a variety of agricultural applications. In Viani et al, 25 fuzzy logic-based DSS system is developed using the WSN for smart irrigation. The fuzzy rule sets are formulated based on the inputs from experienced farmers.…”

Section: Related Workmentioning

confidence: 99%

“…As we know that generally, the environment of agricultural farmlands are associated with signal propagation serious issues such as fading, NLOS, reflection, and multipath propagation. 6,9,25,31,41 The values considered for constants X,Y and Z in Miao et al 20 may not be applicable in the context of the variation in environment size or variation in physical obstacles present in the farmland. Additionally, these values may not be used as it is for other types of crop fields.…”

Section: Fundamentals Of Lnsm and Ofpedm Path Loss Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Efficient localization of target in large scale farmland using generalized regression neural network

Jondhale¹,

Deshpande²

2019

Int J Communication

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Although simple to implement, the traditional trilateration technique is generally associated with significant location estimation errors because of highly nonlinear relationship between Received Signal Strength Indicator (RSSI) and distance. In case of agricultural farmland, there is always noise uncertainty in the RSSI measurements because of signal propagation issues such as NLOS, multipath propagation, and reflection. In the context of such environmental dynamicity, the localization algorithm must be efficient in terms of Localization Accuracy and Execution Speed to provide real-time performance. The Generalized Regression Neural Network (GRNN) is a noniterative highly parallel neural architecture with the capability to get trained quickly using very few training samples. This paper introduces a range free GRNN localization algorithm as an alternative to the traditional range-based trilateration technique for a large scale wheat farmland. This paper also presents the modified Optimal Fitted Parametric Exponential Decay Model (OFPEDM)-based signal path loss model to deal with the issue of environmental dynamicity. The evaluation of localization performance of the trilateration and the proposed GRNN-based approaches is carried out with the help of Wireless Sensor Network (WSN) using three path loss models, namely, Log Normal Shadow Fading (LNSM), Original OFPEDM, and proposed Modified OFPEDM. For all these implementations, the proposed GRNN algorithm demonstrates superior localization performance (localization accuracy of the order of few centimeters) over traditional trilateration irrespective of nonlinear system dynamics, path loss model, and environmental dynamicity. The execution speed of the proposed algorithm is of the order of few milliseconds. KEYWORDS general regression neural network (GRNN), optimal fitted parametric exponential decay model (OFPEDM), received signal strength Indicator (RSSI), wireless sensor network (WSN)

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Smart Irrigation Systems Using Machine Learning and Control Theory

Çetin

Beyhan

2022

The Digital Agricultural Revolution

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Low-Cost Wireless Monitoring and Decision Support for Water Saving in Agriculture

Cited by 130 publications

References 31 publications

Wireless technologies for smart agricultural monitoring using internet of things devices with energy harvesting capabilities

Wireless technologies for smart agricultural monitoring using internet of things devices with energy harvesting capabilities

Efficient localization of target in large scale farmland using generalized regression neural network

Smart Irrigation Systems Using Machine Learning and Control Theory

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