Optimization of soil moisture sensor placement for a PV-powered drip irrigation system using a genetic algorithm and artificial neural network

Dursun, Mahir; Özden, Semih

doi:10.1007/s00202-016-0436-8

Cited by 25 publications

(11 citation statements)

References 29 publications

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“…Considering that all of the nodes were deployed in the citrus orchard with a solar supply, to reduce node energy consumption and packet loss, the tree networking mode was adopted to reduce the data forwarding and increase the communication distance of different nodes as shown in Figure 4. According to Dursun et al's research [22], the measured soil data in the center was closer to the mean value. Thus, five nodes were deployed in the center of each section to represent each SM and SEC, respectively.…”

Section: Software Of Iot Systemmentioning

confidence: 70%

Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM

et al. 2021

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In order to create an irrigation scheduling plan for use in large-area citrus orchards, an environmental information collection system of citrus orchards was established based on the Internet of Things (IoT). With the environmental information data, deep bidirectional long short-term memory (Bid-LSTM) networks are proposed to improve soil moisture (SM) and soil electrical conductivity (SEC) predictions, providing a meaningful reference for the irrigation and fertilization of citrus orchards. The IoT system contains SM, SEC, air temperature and humidity, wind speed, and precipitation sensors, while the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were calculated to evaluate the performance of the models. The performance of the deep Bid-LSTM model was compared with a multi-layer neural network (MLNN). The results for the performance criteria reveal that the proposed deep Bid-LSTM networks perform better than the MLNN model, according to many of the evaluation indicators of this study.

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Section: Software Of Iot Systemmentioning

confidence: 70%

Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM

et al. 2021

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“…These weight values and bias values are tried to be estimated by the developed model. (Dursun & Dzden, 2017). Onputs and output layers' data are obtained from experimental data.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Performance evaluation of GSA, SOS, ABC and ANN algorithms on linear and quadratic modelling of eggplant drying kinetic

Özden

Kiliç

2020

Food Sci. Technol

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On this study, kinetics of eggplant drying was modeled in the laboratory-scaled Food Drying Dven (FDD) with resistance heater was designed and manufactured. The temperature, energy consumption and drying time of FDD were recorded by keeping the temperature of at different temperatures as 40, 50 and 60 °C. These saved values were chosen as the input parameters of the model. The weight value of the eggplant was taken as the output parameter. Linear and quadratic equations were developed for modeling and constant coefficients of these equations were estimated with Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), symbiotic organisms search (SDS) algorithms. On addition, the performances of these models were compared with the model developed with ANN in terms of performance and time. The results show that the lowest error of the developed linear and quadratic equations was obtained with SDS algorithm. The MSE metric results of ANN were fifty times higher than the performance of SDS algorithm, and the SDS algorithm reached best value three times faster than the ANN.

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“…La energía necesaria para alimentar los sistemas de bombeo genera importantes emisiones de gases de efecto invernadero que contribuyen directamente al calentamiento global del planeta (Tarjuelo et al, 2015). El uso de sistemas de riego por goteo utilizando la energía solar ofrece multitud de ventajas, tales como el uso eficiente de los recursos de agua y energía en las zonas de riego agrícola por lo que la implementación de estos sistemas es cada vez más extendida (Dursun et al, 2016…”

Section: 2unclassified

Libro de Actas XXXV Congreso Nacional de Riegos

2017

XXXV Congreso Nacional De Riegos

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Agrohidrología. Hidrología del riego. Relaciones agua-suelo-planta-atmósfera. Necesidades hídricas de los cultivos. Balance de agua. Coeficientes de cultivo. Estrategias de riego en áreas con escasez de agua. Respuesta de los cultivos a riegos deficitarios. Nuevos métodos y tecnologías de medición del agua de riego, herramientas de cálculo y modelos de simulación.Salinidad, Drenaje, Efectos Ambientales y Agua de riego: Adecuación de las características del suelo al método de riego. Efecto del clima y de su posible cambio en las prácticas e infraestructuras de riego. Impacto del regadío y drenaje en el entorno natural. Las prácticas de riego y sus efectos sobre la mejora de la capacidad de reserva de agua en el suelo. Lixiviación de sales y otros elementos. Recuperación de suelos salinos.Calidad del agua de riego (sales, contaminantes). Escasez de agua de buena calidad. Aprovechamiento de aguas subterráneas para riego. Utilización para el riego de aguas depuradas, regeneradas o desalinizadas. Efecto de estas aguas sobre el suelo, cultivos y acuíferos. Riesgos de la utilización de aguas residuales regeneradas en los seres vivos.

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Optimization of soil moisture sensor placement for a PV-powered drip irrigation system using a genetic algorithm and artificial neural network

Cited by 25 publications

References 29 publications

Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM

Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM

Performance evaluation of GSA, SOS, ABC and ANN algorithms on linear and quadratic modelling of eggplant drying kinetic

Libro de Actas XXXV Congreso Nacional de Riegos

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