Design of a pipeline sensor-based platform for soil water content monitoring

Gravalos, İoannis; Moshou, Dimitrios; Loutridis, S.; Γιαλαμάς, Θεόδωρος; Kateris, Dimitrios; Tsiropoulos, Z.; Xyradakis, Panagiotis

doi:10.1016/j.biosystemseng.2012.06.001

Cited by 15 publications

(4 citation statements)

References 21 publications

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“…for arbitrary τ 0 , and nonzero initialization errorsr 0 :=r 0 − r 0 . Equations (11) and (12) imply that the following asymptotic estimate is obtained:…”

Section: The Continuous Formulation Of the L 1 Adaptive Controller Fomentioning

confidence: 99%

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Experimental Validation of an Adaptive Controller for Manipulators on a Dynamic Platform

Reina

Nguyen

Dankowicz³

2020

Robotica

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SUMMARY This paper reports on laboratory and field experimental results for controlled robotic manipulators operating on moving platforms with unmodeled dynamics. The aim is to validate theoretical predictions for the dependence on control parameters of an adaptive control strategy. In addition, the results provide insight into different discretizations of the continuous-time formulation, suggesting the most suitable discretization scheme for hardware implementation. The second set of experimental results, obtained from an implementation of the control framework for synchronization and consensus in networks of robotic manipulators, similarly validate theoretical predictions on the sensitivity to network communication delays.

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“…for arbitrary τ 0 , and nonzero initialization errorsr 0 :=r 0 − r 0 . Equations (11) and (12) imply that the following asymptotic estimate is obtained:…”

Section: The Continuous Formulation Of the L 1 Adaptive Controller Fomentioning

confidence: 99%

“…7 Mobile robots employ vision systems for discriminating weeds from crops, 8 detecting plant water stress, 9 and evaluating calcium deficiency in lettuce. 10 Other robots carry sensors for monitoring water content 11 and measuring moisture. 12 Robotic sprayers are usually used to provide treatments following such inspections.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of an Adaptive Controller for Manipulators on a Dynamic Platform

Reina

Nguyen

Dankowicz³

2020

Robotica

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“…With this method, the accuracy of the sensor is crucial. Several researchers have published work using this method [20], developed an electromagnetic-based mobile soil water content sensor [21] and a mobile dielectric sensor designed for root zone (0-20 cm) moisture measurements to estimate the ET of soil and plant [22]. The estimation of ET using soil moisture sensors relies heavily on the accuracy of the sensors.…”

Section: Introductionmentioning

confidence: 99%

A Method to Estimate Evapotranspiration in Greenhouse Conditions by Artificial Neural Networks Using Limited Climate Parameters

Yuan,

Feng,

et al. 2023

J. Phys.: Conf. Ser.

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Precise estimation of evapotranspiration (ET) within greenhouse environments assumes pivotal significance in the context of effective agricultural water resource management. It has an important influence on rational irrigation management and water conservation. The present study estimates evapotranspiration by artificial neural networks (ANNs) using limited climate parameters with data from Oct.2016 to Nov.2017 from an experimental greenhouse. Using a sigmoid transfer function, two ANN models, 2-5-1 structure and 4-9-1 structure, were established through the algorithm of multi-layer feed-forward and back-propagation. At the same time, moisture sensors installed at different depths of substrate were also used to calculate the amount of evapotranspiration. The standard reference evapotranspiration was provided by a microlysimeter system, which used an electronic weighing scale to continuously sample the amount of water supplied and lost during the experiment. The ANN (2-5-1) model estimated ET with an RMSE of 0.0915 L/d and an R2 0.9201, with two neurons in the input layer corresponding to the daily mean temperature and daily mean humidity, five neurons in the hidden layer and one neuron in the output layer corresponding to the reference evapotranspiration. The ANN (4-9-1) model estimated ET with an RMSE of 0.0592 L/d and an R2 of 0.9622, with four input climate parameters: daily maximum temperature, daily minimum temperature, daily mean temperature, and daily mean humidity, but it had nine neurons for the hidden layer. The results of linear regression analyses of ET estimation between moisture sensors and actual measurement show that the accuracy of moisture sensors is less than the ANN models (RMSE, 0.1129 L/d; R2, 0.8749). Therefore, these results confirmed the ability of ET estimation by ANN models using limited climate parameters in greenhouse conditions.

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“…An off-road vehicle stability traversing over obstacle has been a matter of concern reflected by the numerous studies [9][10][11][12][13][14][15]. Research about tractor stability and dynamics could be experimental, computer simulation or computer simulation and experimentation.…”

Section: Introductionmentioning

confidence: 99%

The induced shock and impact force as affected by the obstacle geometric factors during tire-obstacle collision dynamics

et al. 2016

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.When citing, please reference the published version. Take down policyWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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Design of a pipeline sensor-based platform for soil water content monitoring

Cited by 15 publications

References 21 publications

Experimental Validation of an Adaptive Controller for Manipulators on a Dynamic Platform

Experimental Validation of an Adaptive Controller for Manipulators on a Dynamic Platform

A Method to Estimate Evapotranspiration in Greenhouse Conditions by Artificial Neural Networks Using Limited Climate Parameters

The induced shock and impact force as affected by the obstacle geometric factors during tire-obstacle collision dynamics

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