Irrigation System for Precision Agriculture Supported in the Measurement of Environmental Variables

Cabarcas, Amaury; Arrieta, Cristian; Cermeno, David; Leal, Humberto; Mendoza, Rafael; Rosales, Camilo

doi:10.1109/iestec46403.2019.00125

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Traditionally, irrigation was considered precise if the same amount of water could be distributed evenly over the entire surface area, without taking into account the spatial variability of the soil and vegetation (Cabarcas et al, 2019). In contrast, precision irrigation pursues the objective of adapting water supplies to actual crop needs on a small scale (Fernández et al, 2019;Jiménez et al, 2022).…”

Section: The Role Of Innovations In Irrigationmentioning

confidence: 99%

Exploring innovation adoption behavior for sustainable development of Mediterranean tree crops

Timpanaro

Pecorino

Chinnici

et al. 2023

Front. Sustain. Food Syst.

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IntroductionThe combination of knowledge, personal skills and company resources influences, all things being equal, such as the availability of new technologies, market conditions and other factors external to the company, farmers in their innovation choices. This study is an attempt to understand which psychological constructs influence the decision-making process of farmers specialized in typical Mediterranean crops with regard to innovation. Previous studies on the adoption of agricultural innovations have often considered socio economic characteristics and ignored the underlying motivational factors that influence the behavioral intention of farmers.MethodsThis study adopted three socio-psychological constructs, Attitude (ATT), Subjective Norm (SN), and Perceived Behavioral Control (PBC), derived from the Theory of Planned Behavior (TPB), and proposed three new constructs, Perceived Innovations Characteristics (PIC), Benefits (B), and Transferability (T), thus using an Extended Model of the Theory of Planned Behavior.ResultsThe outcome of the multiple regression revealed that farmers' intention (I) to adopt sustainable irrigation innovations is positively influenced by attitude (ATT), subjective norm (SN), and perceived innovation characteristics (PIC). This last construct had mediating effects on the indirect relationships between PBC, benefits (B), transferability (T), and intention (I).DiscussionThe results provide numerous insights, useful both for outlining the demand for innovation and for calibrating future policies aimed at the primary sector, especially on the sustainable management of irrigation resources. In particular, the analyses carried out highlight the importance of factors external to the company as key levers in shaping the demand for innovations.

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Section: The Role Of Innovations In Irrigationmentioning

confidence: 99%

Exploring innovation adoption behavior for sustainable development of Mediterranean tree crops

Timpanaro

Pecorino

Chinnici

et al. 2023

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

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“…El IoT en la agricultura se está expandiendo rápidamente, combinando software y hardware que ofrecen soluciones optimas en el control de riego (Abioye et al, 2020), estos sistemas pueden utilizar una variedad de sensores siendo los más utilizados los de temperatura, humedad del suelo y humedad del ambiente (Cabarcas et al, 2019), debido a que se necesitan los datos recogidos para la construcción de la lógica que hará funcionar al sistema y ayudará a la toma de decisiones del mismo (Math et al, 2019;Rudy Hendrawan et al, 2019;Wakhare et al, 2020). Genera beneficios en el control del proceso, reduce la carga laboral de los agricultores y optimiza el proceso (Cabarcas et al, 2019)., también se cuenta entre sus beneficios el monitoreo, debido a que permite que el usuario pueda controlar el sistema desde el lugar donde se encuentre y a cualquier hora (Abioye et al, 2020), se puede conocer la variación de las variables medioambientales tales como la temperatura, la humedad del ambiente y el suelo, para mejorar la toma de decisiones del sistema (Cabarcas et al, 2019), además el uso de IoT ayuda a la reducción del costo de operación y mejora la productividad tanto en la agricultura como en las actividades que realiza el usuario (Math et al, 2019). También, se puede fusionar con las redes neuronales artificiales, estas son utilizadas para el análisis y la generación de resultados con mucha precisión y el poder analizar grandes cantidades de datos de una forma muy rápida, de esta manera el sistema sería totalmente automatizado, sabiendo los sectores que tiene que regar, que dispositivos usará y disminuyendo en su totalidad la actividad humana (Aggarwal & Kumar, 2019).…”

Section: Iot Aplicado a Riego Automatizadounclassified

“…La agricultura es uno de los pilares de la economía en países subdesarrollados, sin embargo, para lograr un manejo óptimo es necesario la utilización de tecnología (Cabarcas et al, 2019). En la actualidad el riego manual no es eficiente, el desperdicio de agua y el exceso de humedad dificultan el crecimiento del cultivo y genera pérdidas en las cosechas (Vijay et al, 2020), una alternativa para mejorar las condiciones de los cultivos es el uso de la tecnología.…”

Section: Introductionunclassified

Una revisión de procesos de implementación para sistemas de riego automatizado

Carrasco¹,

Ygnacio²

2022

recibe

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La agricultura es un pilar fundamental en la economía de muchos países, para lograrlo se debe tener en cuenta la inversión en tecnología y los factores que hacen posible su desarrollo. Los sistemas de riego automatizado facilitan la labor de los agricultores, mejorando la economía y el estilo de vida de las personas. El objetivo del presente trabajo es determinar las tecnologías y técnicas más importantes a ser utilizados para la construcción de sistemas de riego automatizado, para ello se realiza una revisión sistemática de literatura utilizando el método SALSA que describe el proceso en cinco pasos, se utilizó las bases de datos IEEE Xplore y Science Direct, se hizo el análisis cuantitativo de la muestra seleccionada, identificando las técnicas y tecnologías más utilizadas en estos procesos, además se determinó la problemática existente a partir de un análisis cualitativo, identificándose factores que deben ser tomados en cuenta en estas implementaciones.

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“…The use of precision farming using technical tools as a guide focuses on developing automated irrigation systems and ancillary technology for crop irrigation. 21 Irrigation systems can use sensors to determine environmental variables such as temperature, soil moisture, and relative humidity in the service environment. 22 The design of the intelligent farm automatic organic irrigation system is realized by Fuzzy logic (FL) control, such as automatic opening of the electric pump reservoir and water storage status of power management from the reservoir to the farm irrigation system.…”

Section: Related Workmentioning

confidence: 99%

Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

Vivekanandhan¹,

Sakthivel²,

Manikandan³

2021

Computational Intelligence

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Traditional agricultural systems require huge amounts of money for on‐site irrigation power. Irrigation is the process of giving water to plants for growth and development. The cost of global warming creates the potential effects of water adaptation measures to ensure food production and water availability for consumption. It requires continuous monitoring of soil moisture content at the root zone and initiating irrigation as per the pre‐programmed schedule depending upon the plant's nature, growth, soil type, and environment. Thus, the cost to farmers using conventional drip irrigation has become so high that they have to go manually and monitor the land frequently. So when the previous method was used for irrigation, it did not give correct classification results. Agricultural data mining technology provides the best crops for travel water to increase the number of crops produced. Agricultural data can be collected more efficiently using data collection and capable analytics sensors. This proposed work introduces an adaptive neuro‐fuzzy inference system (ANFIS) technique for analyzing agricultural plant growth based on soil, water level, temperature, and moisture conditions. The proposed technique has three phases: environment data collection using sensor node, preprocessing, feature selection, and classification. The initial phase captures the plant's environment condition via a sensor device, then transmits data to servers with the help of an edge node or gateway. This recursive adaptive filter method normalizes the data and removes irrelevant noise data from the sensor node. The feature selection method to extract the environment feature value with the help of a linear regression model. The ANFIS method's plant environment feature value has trained the neurons with the help of the stochastic gradient descent method. The classification method introduces a fuzzy rule to compute and validate the input parameters (e.g., soil moisture, temperature, and humidity) to predict any environment value changes. The comparison results prove that efficient monitoring is obtained through the proposed smart irrigation system.

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Irrigation System for Precision Agriculture Supported in the Measurement of Environmental Variables

Cited by 7 publications

References 12 publications

Exploring innovation adoption behavior for sustainable development of Mediterranean tree crops

Exploring innovation adoption behavior for sustainable development of Mediterranean tree crops

Una revisión de procesos de implementación para sistemas de riego automatizado

Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

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