Simulation of nitrate distribution under drip irrigation using artificial neural networks

Li, Jiusheng; Yoder, R.; Odhiambo, Lameck O.; Zhang, J.

doi:10.1007/s00271-003-0090-6

Cited by 43 publications

(14 citation statements)

References 21 publications

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“…The new approaches to describing the systemdependent upper boundary conditions were established for different soils with and without ponded areas around the emitter (Li et al, 2005b) and verified by laboratory observations (Li et al, 2003b). The model has been successfully used in subsequent research (Wang et al, 2014b;Wen et al, 2017).The continuous efforts contributed to the testing of a non-deterministic approach to using artificial neural networks (ANNs) for modelling nitrate distribution under drip fertigation testing (Li et al, 2004a). In selecting the initial soil water content, initial nitrate concentration in soil, emitter discharge rate, input concentration of the fertilizer, applied volume and final soil water content as the input parameters, the model predicted the nitrate concentrations in the soil after fertigation with an acceptably high accuracy.…”

Section: Water and Nitrogen Dynamics In Soil Under Drip Fertigationmentioning

confidence: 99%

Increasing Crop Productivity in an Eco‐Friendly Manner by Improving Sprinkler and Micro‐Irrigation Design and Management: A Review of 20 Years' Research at the IWHR, China

2017

Irrigation and Drainage

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The use of sprinkler and micro‐irrigation has progressed continuously from being a novelty that was employed by researchers to being widely accepted, efficient methods of irrigation for many crops during the past 30–40 years in China. This paper reviews the studies that have been conducted on sprinkler and micro‐irrigation at the WatSave Lab in the China Institute of Water Resources and Hydropower Research (IWHR) over the past two decades. These studies address several areas of concern and provide partial answers to the questions raised during the application and extension of these relatively new irrigation technologies. With the aim of exploring the consumption of sprinkled water that is intercepted by the canopy, the amount of water interception and the losses were quantified, confirming that sprinkler irrigation is an efficient irrigation method. The standards of sprinkler and micro‐irrigation uniformity were studied with respect to many aspects of water and solute dynamics, environmental effects, and crop yield and quality under a wide range of environments from arid to subhumid climates for typical wheat, cotton, maize and vegetable crops. As a result, new target uniformity values have been recommended. Other studies on water, solute and bacterial transport from normal water and sewage effluent can also be useful in the design, operation, maintenance and management of irrigation systems. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Water and Nitrogen Dynamics In Soil Under Drip Fertigationmentioning

confidence: 99%

Increasing Crop Productivity in an Eco‐Friendly Manner by Improving Sprinkler and Micro‐Irrigation Design and Management: A Review of 20 Years' Research at the IWHR, China

2017

Irrigation and Drainage

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“…The ANN models are generalized mathematical models that use a series of neurons or nodes interlinked by weighted connections to simulate human cognition as it applies to pattern identification and prediction (Fausett 1994;Haykin 1999;Li et al 2004). The researcher should consider R 2 , residual mean square error (RMSE), the number of hidden nodes, tours, and the overfit penalty in the development of an ANN model with JMP IN statistics software (SAS 2002;Minasney 2004).…”

Section: Artificial Neural Network Modellingmentioning

confidence: 99%

Artificial Neural Network Modelling of Leaf Water Potential for Potatoes Using RGB Digital Images: A Greenhouse Study

Zakaluk

Ranjan

2007

Potato Res.

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Plant water status information of potato (Solanum tuberosum L. cv. Russet Burbank) is needed at the farm level for irrigation scheduling. This research investigated the feasibility of using a 5-megapixel digital camera to determine the leaf water potential (Ψ L ) of potato plants by capturing red, green, blue (RGB) digital images in the visible region of the electromagnetic spectrum. A greenhouse experiment was conducted in containerized cv. Russet Burbank potato plants subjected to five soil nitrate-nitrogen (N) levels and four soil water content levels. An artificial neural network (ANN) model, built with RGB images, RGB image transformations, RGB vegetation indices, and principal components analysis, found that for the validation data set, the measured Ψ L and predicted Ψ L results were from common populations. Other results showed: (1) a linear trend between soil nitrate-N levels and leaf reflectance in the G image band, (2) that the RG image bands were more suitable than the B image band for classifying leaf pigment from leaf shadow and leaf damage, (3) soil nitrate-N interacted with leaf greenness, affecting Ψ L prediction, and (4) some image variables were more important than others in the ANN model. Although this greenhouse research shows promise, further field-based research is required to validate the selection of input neurons used and also validate the use of ANN modelling to determine Ψ L at the plant canopy level with cv. Russet Burbank and other cultivars. In addition, an image acquisition method needs to be developed to obtain periodic representative sample coverage over a field.

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“…For this reason Li et al (2004b) proposed an alternative methodology which combines artificial neural networks (ANN) and laboratory experiments. Seventeen column experiments with varying discharge rates and varying input fertilizer concentrations were conducted to provide a database to establish ANN architecture.…”

Section: Modeling and Case Studiesmentioning

confidence: 99%

Environmental Effects of Irrigation in Arid and Semi-Arid Regions

Fernández-Cirelli

Arumí²,

Rivera³

et al. 2009

Chilean J. Agric. Res.

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This article reviews the state of the art with respect to the environmental effects of irrigated agriculture on water and soil quality in arid and semi-arid regions on a field scale. Information is scarce and fragmentary. Examples in selected areas of other arid and semi-arid regions in the world clearly show the importance of studying the environmental impact of irrigation practices on water and soil quality. Studies mainly refer to waterlogging and salinization. As regards agrochemicals, fertilizers have been taken into account through nitrate leaching. The impact of micropollutants such as pesticides and heavy metals on water and soil quality is studied mainly by modeling. Pharmaceutical compounds emerge as pollutants when wastewater is used for irrigation without any previous treatment, situation which is now the object of new studies. There is an obvious need to study in-depth knowledge related to appropriate technologies for the use, treatment, and reuse of wastewater which is a valuable resource in arid and semi-arid regions.

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Simulation of nitrate distribution under drip irrigation using artificial neural networks

Cited by 43 publications

References 21 publications

Increasing Crop Productivity in an Eco‐Friendly Manner by Improving Sprinkler and Micro‐Irrigation Design and Management: A Review of 20 Years' Research at the IWHR, China

Increasing Crop Productivity in an Eco‐Friendly Manner by Improving Sprinkler and Micro‐Irrigation Design and Management: A Review of 20 Years' Research at the IWHR, China

Artificial Neural Network Modelling of Leaf Water Potential for Potatoes Using RGB Digital Images: A Greenhouse Study

Environmental Effects of Irrigation in Arid and Semi-Arid Regions

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