Academic literature on the topic 'Irrigation intelligente'
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Journal articles on the topic "Irrigation intelligente"
Han, Liang, Shi Chang Fu, and Hui Hui Hong. "A Study on the Intelligent Bladder Irrigation Technology." Applied Mechanics and Materials 551 (May 2014): 638–41. http://dx.doi.org/10.4028/www.scientific.net/amm.551.638.
Full textVilla Quisphe, Manuel William, José Augusto Cadena Moreano, and Juan Carlos Chancusig Chisag. "Artificial intelligence: prototype of an automated irrigation system for the cultivation of roses in Cotopaxi." Data and Metadata 3 (June 30, 2024): 398. http://dx.doi.org/10.56294/dm2024398.
Full textFedosov, A. Yu, and A. M. Menshikh. "Implementation of Artificial Intelligence in Agriculture to Optimize Irrigation." Agricultural Machinery and Technologies 16, no. 4 (December 13, 2022): 45–53. http://dx.doi.org/10.22314/2073-7599-2022-16-4-45-53.
Full textJahanavi, G., D. Meghana, M. Bhanu Prakash, K. Vamsi, and D. Padmavathi. "Intelligent Irrigation Systems: A Review Of IoT-Enabled Smart Irrigation Technologie." International Journal of Research Publication and Reviews 5, no. 11 (January 2024): 7229–41. https://doi.org/10.55248/gengpi.5.1124.3420.
Full textGulomjonovich, Goyipov Umidjon, and O‘rmonov Musoxon Nodirjon o‘g‘li. "FUNDAMENTALS OF DESIGNING INTELLIGENT IRRIGATION SYSTEMS." European International Journal of Multidisciplinary Research and Management Studies 4, no. 10 (October 1, 2024): 46–49. http://dx.doi.org/10.55640/eijmrms-04-10-07.
Full textShaglouf, Mohamed M., Mostafa A. Benzaghta, Hassin AL. Makhlof, and Moftah A. Abusta. "Scheduling Drip Irrigation for Agricultural Crops using Intelligent Irrigation System." Journal of Misurata University for Agricultural Sciences, no. 01 (October 6, 2019): 244–55. http://dx.doi.org/10.36602/jmuas.2019.v01.01.19.
Full textArlanova, A. A., B. A. Hojamkuliyeva, N. Sh Babanazarov, and M. S. Arlanov. "Artificial intelligence for smart irrigation: Reducing water consumption and improving agricultural output." E3S Web of Conferences 623 (2025): 04001. https://doi.org/10.1051/e3sconf/202562304001.
Full textZhai, Zhiyong, Xing Chen, Yubin Zhang, and Rui Zhou. "Decision-making technology based on knowledge engineering and experiment on the intelligent water-fertilizer irrigation system." Journal of Computational Methods in Sciences and Engineering 21, no. 3 (August 2, 2021): 665–84. http://dx.doi.org/10.3233/jcm-215117.
Full textLoubna, Hamami. "Wireless Sensor Network Application for Intelligent Irrigation System." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (February 28, 2020): 163–73. http://dx.doi.org/10.5373/jardcs/v12sp3/20201250.
Full textK. C. Jayasankar, G. Anandhakumar, and A. Kalaimurugan. "Fuzzy Logic Controller-based Intelligent Irrigation System Using Solar Radiation Data." Journal of Environmental Nanotechnology 13, no. 2 (July 1, 2024): 37–45. http://dx.doi.org/10.13074/jent.2024.06.242586.
Full textDissertations / Theses on the topic "Irrigation intelligente"
Pisanò, Lorenzo. "IoT e Smart Irrigation: gestione dei Big Data attraverso un sistema di notifica intelligente." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23531/.
Full textHammouch, Hajar. "Application of advanced artificial intelligence models to manage irrigation using sensor data and satellite images." Electronic Thesis or Diss., Institut polytechnique de Paris, 2025. http://www.theses.fr/2025IPPAS005.
Full textIn this thesis, we tackle the urgent challenges in water management, agricultural image analysis, by the investigation of artificial intelligence (AI) models to optimize precision farming. Recognizing the growing threat of climate change and the global water crisis, we conducted a systematic review of smart irrigation technologies, focusing on IoT sensors, remote sensing, and AI methods. This comprehensive review not only highlights existing approaches but also sets the stage for new solutions that optimize water use and enhance agricultural sustainability. To address the critical lack of agricultural datasets, we proposed and implemented convolutional neural networks (CNNs) and generative adversarial networks (GANs) to predict soil moisture from UAV-captured aerial images. By proposing a novel GAN model that generates conjointly synthetic images and their continuous ground truth vectors, we significantly enhanced CNN performance in data-scarce environments. This significantly reduced prediction errors, proving the power of GAN-driven data augmentation in regression tasks, a data augmentation setting not handled by conventional GANs. Additionally, we have proposed Hybrid AI models, combining deep learning models with Machine learning models leveraging human expert-based features, for predicting nitrogen content in sorghum crops—an essential factor for crop health—using UAV-captured RGB imagery. We have carried out this research in the context of collaboration with the Czech University of Life Sciences in Prague (CZU) and the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in Telangana, India. By integrating spectral indices with CNN architectures, we enhanced the accuracy of nitrogen predictions, supporting more precise and sustainable agricultural practices. Through the fusion of IoT, AI, and RS technologies, our work provides innovative solutions to address critical challenges in water resource management and environmental sustainability
GonÃalves, Fabricio Mota. "Tools for analysis of self-management and water use of sustainability in irrigation perimeters." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=16108.
Full textThis work aims to characterize the current stage of Irrigated Perimeters Federal government with a view to self-management process and present alternative of allocating water distribution in secondary irrigation canals. The research was divided into two themes. The first addressed the development of a methodology for evaluating the performance of Irrigated Perimeters from the creation of a statistical model Multivariate discriminant and an Artificial Neural Network using the performance indicators of irrigated public areas of the National Department of Works Against Drought (Dnocs) and Development Company of the SÃo Francisco and ParnaÃba (Codevasf) as a way to evaluate the prospect of self-management of the same. The second dealt with the optimization of water use, a case study at the Experimental Farm Curu Valley, belonging to the Federal University of CearÃ, in the area adjacent to the irrigated Curu Pentecost were accomplished. Based on information provided by the National Department of Works Against Drought (Dnocs) and Development Company of the SÃo Francisco and ParnaÃba (Codevasf), the key performance indicators relating to Self-Management of Irrigated Perimeters were evaluated. The Multivariate and discriminant analysis (AMD) technique Artificial Neural Networks (ANN) were used to separate the standards relating to the performance of Irrigated Perimeters linear character or not. RNA yielded the automatic identification of the pattern that belongs to each perimeter over time. Based on the results obtained in the multivariate discriminant analysis, we observed the Generation Revenue per Hectare (HRM) as the most important indicator in discriminatory process between Irrigated Perimeters regarding self-management. The perimeters with the best performance in relation to self-management were: Nilo Coelho, CuraÃÃ I Pirapora and ManiÃoba. Regarding the operationalization of water use, we used a mathematical model of linear programming to determine the most rational way to release water for irrigated areas. The allocation defined by mathematical modeling proved adequate for the needs of established cultures, showing the most rational use of water.
Este trabalho tem como objetivo caracterizar o estÃgio atual dos PerÃmetros Irrigados PÃblicos Federais com vistas ao processo de autogestÃo e apresentar alternativa de alocar a distribuiÃÃo de Ãgua em canais secundÃrios de irrigaÃÃo. A pesquisa foi dividida em dois temas. O primeiro abordou o desenvolvimento de uma metodologia de avaliaÃÃo de desempenho de PerÃmetros Irrigados a partir da criaÃÃo de um modelo estatÃstico Discriminante Multivariado e de uma Rede Neural Artificial utilizando os indicadores de desempenho dos perÃmetros pÃblicos irrigados do Departamento Nacional de Obras Contra as Secas (Dnocs) e da Companhia de Desenvolvimento do Vale do SÃo Francisco e ParnaÃba (Codevasf), como forma de avaliar a perspectiva da autogestÃo dos mesmos. O segundo tratou da otimizaÃÃo do uso da Ãgua, tendo sido realizado um estudo de caso na Fazenda Experimental Vale do Curu, pertencente à Universidade Federal do CearÃ, em Ãrea contÃgua ao PerÃmetro Irrigado Curu Pentecoste. Com base nas informaÃÃes disponibilizadas pelo Departamento Nacional de Obras Contra as Secas (Dnocs) e a Companhia de Desenvolvimento do Vale do SÃo Francisco e ParnaÃba (Codevasf), foram avaliados os principais indicadores de desempenho relativos à AutogestÃo dos PerÃmetros Irrigados. A AnÃlise Multivariada Discriminante (AMD) e a tÃcnica de Redes Neurais Artificiais (RNA) foram utilizadas para separar os padrÃes referentes ao desempenho dos PerÃmetros Irrigados de carÃter linear ou nÃo. A RNA proporcionou a identificaÃÃo automÃtica do padrÃo a que pertence cada perÃmetro no decorrer do tempo. Com base nos resultados obtidos na AnÃlise Multivariada Discriminante, observou-se o indicador GeraÃÃo de Receita por Hectare (GRH) como mais importante no processo discriminatÃrio entre os PerÃmetros Irrigados quanto à AutogestÃo. Os PerÃmetros com os melhores desempenhos em relaÃÃo à AutogestÃo foram: Nilo Coelho, CuraÃà I, Pirapora e ManiÃoba. Com relaÃÃo à operacionalizaÃÃo do uso da Ãgua, utilizou-se um modelo matemÃtico de programaÃÃo linear para determinar a forma mais racional de liberar Ãgua para as Ãreas irrigadas. A alocaÃÃo definida pela modelagem matemÃtica mostrou-se adequada para as necessidades das culturas estabelecidas, mostrando a utilizaÃÃo mais racional da Ãgua.
Granier, Jacques. "Un système d'ingénierie assisté par ordinateur pour la conception des équipements d'irrigation : une application réalisée en langage SNARX." Montpellier 2, 1990. http://www.theses.fr/1990MON20156.
Full textNeto, OdÃlio Coimbra da Rocha. "Rede Neural artificial aplicado ao manejo de irrigaÃÃo." Universidade Federal do CearÃ, 2012. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7930.
Full textA irrigaÃÃo à uma das prÃticas culturais que mais influencia o aumento da produÃÃo. No entanto, para o sucesso desta prÃtica necessita-se determinar o tempo certo da aplicaÃÃo de Ãgua para evitar desperdÃcios. Com isso, o emprego de sensores de umidade, como os sensores capacitivos, para nÃveis reais de umidade do solo aliados a redes neurais artificiais (RNAs) que calculam tempo de irrigaÃÃo, podem ser uma aquisiÃÃo promissora para a automaÃÃo de sistemas de irrigaÃÃo. Desta forma, objetivou-se com o presente trabalho desenvolver uma RNA que estime o tempo de irrigaÃÃo e comparando-o com o tempo estimado pelo mÃtodo do balanÃo volumÃtrico para a cultura da melancia. Foram utilizadas RNAs do tipo perceptron de mÃltiplas camadas. Para o treinamento foram usados dados de manejos em Ãrea do PERÃMETRO IRRIGADO BAIXO ACARAà no estado do Cearà onde a umidade do solo à determinada por sensores capacitivos desenvolvidos pela Universidade Federal do Cearà (UFC). Foram testadas redes para as fases da cultura. A primeira fase determinada entre 0 e 30 dias apÃs a semeadura (DAS) e a segunda fase sendo de 31 à 60 DAS. Foram testadas redes com 2 e 4 entradas; com 5, 10 e 20 neurÃnios na camada intermediÃria (NCI) e 1.000, 5.000 e 10.000 iteraÃÃes. ApÃs os treinamentos, as redes neurais artificiais foram testadas em campo para a sua validaÃÃo, comparando as suas respostas em relaÃÃo ao mÃtodo do balanÃo hÃdrico volumÃtrico (BHV) para a segunda fase da cultura. Avaliando as redes com 2 e 4 entradas, observou-se que as redes de 4 entradas obtiveram menor erro quadrÃtico mÃdio, convergindo mais rapidamente para valores prÃximos a zero, quando comparadas Ãs redes de 2 entradas. Quanto ao NCI, nÃo houve mudanÃas entre as redes, dispensando a necessidade de programar redes maiores que 5 NCI para essa aplicaÃÃo. Para o nÃmero de Ãpocas de treinamento, a que obteve o melhor ajuste aos valores foram as redes com 10.000 iteraÃÃes para a primeira fase da cultura e 5.000 iteraÃÃes para a segunda fase da cultura. Com a etapa de campo pode-se constatar que nÃo houve diferenÃa estatÃstica entre os dois manejos adotados. Assim, a rede neural artificial mostrou-se eficiente para o manejo da irrigaÃÃo, mesmo tendo no experimento valores inÃditos ao treinamento. Neste trabalho pode-se concluir que a RNA de melhores respostas para a primeira fase da cultura apresentou a MLP 4-5-1 com 10.000 Ãpocas de treinamento e taxa de aprendizagem de 0,9 e para a segunda fase, MLP 4-5-1, com 10.000 Ãpocas de treinamento e taxa de aprendizagem de 0,9. Conclui-se tambÃm, com a etapa de campo, que a rede foi bem sucedida em calcular o tempo de irrigaÃÃo.
Irrigation is an agricultural practice that leads to high crop production, however the success of this practice depends largely on correct computation of the timing of the application to avoid excessive or deficit application. Thus, the use of moisture sensors, such as capacitive sensors for determining soil moisture combined with artificial neural networks (ANNs) to calculate irrigation time can be a promising tool for automation of irrigation systems. The objective of this work was to develop an ANN that estimate the irrigation time and to contrast the results with the management based on a volume balance method on a watermelon field. Multilayer perceptron types of ANNs were tested. For ANNs training, data obtained in previous harvest were used. The watermelon field was located in Baixo Acaraà Irrigation District in Cearà State â Brazil, where soil moisture was determined using capacitive sensors developed at the Universidade Federal do Cearà (UFC). Networks were tested for two growing stages. The first stage spanning from 0 to the 30th day after seeding (DAS) and the second stage from the 31st to 60th DAS at harvesting. Networks were tested with 2 and 4 inputs, with 5, 10 and 20 neurons in the intermediate layer (NIL) and 1,000, 5,000 and 10,000 epochs. Upon training, the artificial neural networks were field-tested for validation by comparing their responses to the volumetric water balance method (VWBM) for the second stage an succeeding crop cycle. It was found that networks with four entries presented the largest mean square error, converging rapidly to values close to zero, compared the networks with two entries. For the NIL, it was not found significant difference in the mean square error between all 3 tested architectures, therefore it was not necessary to test networks larger than 5 NIL for this application. For the number of training epochs, the one with the best fit values were networks with 10,000 epochs for the first stage of the crop cycle, and 5,000 epochs for the second stage of the crop cycle. It was found no statistical difference in watermelon yield between the two irrigation timing strategies tested (ANN and VWBM). Therefore the artificial neural network was efficient in irrigation management in the field even though the network was presented to some values not occurring during the training process. Thus, one can conclude that the ANN for best performance was a 4-5-1 with learning rate 0.9, and 10000 and 5000 training epochs, respectively in the first and second crop stage. In addition, it was found that the network successfully scheduled the irrigation during the validation process.
Boško, Blagojević. "Minimizacija odstupanja grupne od individualnih odluka primenom inteligentnih stohastičkih algoritama u problemima vodoprivrede i poljoprivrede." Phd thesis, Univerzitet u Novom Sadu, Poljoprivredni fakultet u Novom Sadu, 2015. https://www.cris.uns.ac.rs/record.jsf?recordId=94181&source=NDLTD&language=en.
Full textAgricultural and water management decision problems are usually complex because many criteria (suchas economical, social and environmental) need to be considered. For this kind of problems, decisionmaking process is often based only on qualitative data or sometimes on combination of quantitative andqualitative data. The Analytic Hierarchy Process (AHP) is a multi criteria decision-making method thathas been used in many applications related with decision-making based on qualitative data, and isapplicable to both individual and group decision making situations. Because of the increasingcomplexity of decision making problems in agriculture and water management and the necessity toinclude all interested participants in problem solving, nowadays many AHP decision making processestake place in group settings. There are various aggregation procedures for obtaining a group priorityvector within AHP-supported decision making, the most common of which are the aggregation ofindividual judgments (AIJ), aggregation of individual priorities (AIP) and aggregations based onconsensus models.A heuristic stochastic approach to group decision making is proposed in this dissertation as anaggregation procedure which searches for the best group priority vector for a given node in an AHP–generated hierarchy. The group Euclidean distance (GED) is used as a group consistency measure forderiving the group priority vector for a given node in the AHP hierarchy where all participatingindividuals already set their judgments. The simulated annealing (SA) algorithm tries to minimize theGED, of the process of which can be considered an objective search for maximum consensus betweenindividuals within the group. The group priority vector obtained in this way is invariant to anyprioritization method; that is, there is no need to have individual priority vectors as is required by someother aggregation procedures. This approach is named simulated annealing aggregation procedure(SAAP). In order to check validity of this approach, three examples are used to compare it's results withresults obtained by various combinations of aggregations (AIJ and AIP), consensus models andprioritization methods. In this dissertation, SAAP and other known combinations of aggregationprocedures and prioritization methods are labeled as aggregation schemes. Results shows that the SAAPperforms better or at least equally to several other well known combinations of prioritization andaggregation in AHP group decision making frameworks.The second objective of this dissertation was to establish a transferable and transparent procedure formulti criteria group evaluations of land suitability for irrigation. The multi criteria approach isrecommended because according to FAO documents all aspects of the problem (environment, socialaspect, economy) need to be considered in the evaluation, not just soil. To make a decision on where tobuild new, sustainable irrigation systems, here we propose multi criteria group decision makingapproach which combines AHP and Geographic Information System (GIS). This approach is presentedas four-phase decision making framework. In the first phase, subcriteria relevant in validating landsuitability were grouped into five major criteria: soil, climate, economy, infrastructure and environment.Considered as spatially determined decision making elements, criteria and subcriteria were evaluatedwithin the AHP framework by identified experts in the subject area.In the second phase new multi criteria method is developed for deriving decision makers' weights. Usingthis weights and individual priority weights of subcriteria from first phase final group weights ofsubcriteria (GIS layers) are computed. In third phase each subcriterion (GIS layer) is standardized. Then,the cell values in each of the subcriterion layers are multiplied by the corresponding final weights of thesubcriteria and aggregated into the final land suitability maps for irrigation in GIS environment. Finally,in the fourth phase, a sensitivity analysis is applied to check the influence of different criteria on theresult. By changing the weights of criteria, two more maps were generated showing land suitability forirrigation regarding natural conditions and economy-water infrastructure.
LIAO, JIA-QING, and 廖家慶. "Intelligent Plant Irrigation System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/24j3cy.
Full text南開科技大學
電子工程研究所
106
In a time of pursuing global urban sustainable development goal, people pay increasing attention to the urban ecological environment and residential quality. In recent years, however, the city population has been getting higher and higher as the rapidly expanding world population. Gradually, green space has been substituted with great numbers of tall buildings. It is the severe shortage of park and green space that cause a serious urban heat island effect. Therefore, building a green residential environment has become imperative. To make the greening residential environment much easier, we established Intelligent Plant Irrigation System by using Webduino. The system is separated into two parts, Inductive Terminal and Power Control Terminal, that make the system established easier. Inductive Terminal gathers information and provides the information for Power Control Terminal and users to judge. To achieve the best management control, users can adjust settings to adapt to the environment where the users are. Through the Internet, users can also monitor data and change settings from computer and mobile device timely.
Pires, Maria Inês Soares de Matos dos Santos. "Intelligent rainwater reuse system for irrigation." Master's thesis, 2020. http://hdl.handle.net/10071/21754.
Full textThe technological advances in the area of Internet of Things have been creating more and more solutions in the area of agriculture. These solutions are quite important for life, as they lead to the saving of the most precious resource, water, being this need to save water a concern worldwide. The dissertation proposes the creation of an Internet of Things system, based on a network of sensors and interconnected actuators that automatically monitors the quality of the rainwater that is stored inside a tank, in order to be used for irrigation. The main objective is to promote sustainability by reusing rainwater for irrigation systems, instead of water that is usually available for other functions, such as other productions or even domestic tasks. A mobile application was developed, for Android, so that the user can control and monitor his system, in real time. In the application it is possible to visualize the data that translate the quality of the water inserted in the tank, as well as perform some actions on the implemented actuators, such as start/stop the irrigation system and pour the water in case of poor water quality. The implemented system translates a simple solution with a high level of efficiency and tests and results obtained, within the possible environment
Chen, Ling-Hsi, and 陳令錫. "The Study on Evapotranspiration Model of Intelligent Irrigation Management for Horticultural Crop in Protected Culture." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/de3k6n.
Full text國立中興大學
生物產業機電工程學系所
106
The irrigation technology with evapotranspiration (ET), base on the water balance, is the global tendency for water-saving. This study adapted the lysimeter theory and utilized the weighing balance, data logger and weather sensors for monitoring of seasonal horticulture. The weighing raw data were constituted with two components: evapotranspiration and irrigation operation. The irrigation component was replaced by nearby average values with data smoothing technique to become the target evapotranspiration samples. The target evapotranspiration samples were divided into training data sets and testing data sets. The regression of R2 for estimating dependent variable ET of five independent variables (Rn, VPD, T, RH, Ws) or two independent variables (Rn, VPD) or single independent variables either Rn or VPD are all R2 above 80%. However, the R2 value of the single independent variables either T or RH was lower than 70%, that indicated the estimating ET performance was unstable. The wind speed inside greenhouse was low, so that its effect of estimating ET was small. Using a single VPD parameter to estimat ET has good linear correlation, the R2 was 88.4%. The effect of different regions are not significant. However, the single Rn to estimat ET was affected with different regions. The reason could be explained that the weak Rn for temper region and strong Rn for subtropical weather of Taiwan. The plants’ ET data are the basic information for rationally irrigation decision strategy. The evapotranspiration of single plant of eustoma, tomato and cucumber were conducted for several years’ experiments. ET of tomato and cucumber plant are increase with accumulation of solar radiation intensity and VPD under light saturation point on sunny days. ET will decrease with the change of cloud thickness. Mean square error (MSE) value was used to estimate the performance of artificial intelligent (AI). In training data set, the RMSE of AI is 0.597(MSE=0.356). The value was bigger than 0.380 of the RMSE of five independent variables with linear regression. In testing data set, the RMSE of AI was 0.530(MSE=0.281). The value was bigger than 0.281 of the five independent variables with linear regression. It shows AI has excellent eatimation ET performance. According to the experimental results, development of the Light Accumulation Irrigation Trigger Device (LAITD) was suitable for Taiwan farming. The features of LAITD are low installation and maintenance cost, approach to the ET technology, sufficient irrigation on sunny day, automatic reduced irrigation on raining day, and intensively irrigation around midday when high light shining. The LAITD enhance to a timely, appropriate and rational irrigation technique.
LIN, YE-ZE, and 林曄澤. "An Intelligent Constant-Humidity Irrigation System based on the Big Data Analytics: Design and Implementation." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/v4e2xe.
Full text國立虎尾科技大學
資訊工程系碩士班
107
In recent years, global climate anomalies have caused climate turmoil, and agriculture has become more difficult to survive under the influence of climate anomalies. Fortunately, with the advancement of science and technology and the popularization of the internet of things, there have been research systems designed to automatically irrigate systems, but many papers use uniform irrigation. The uniform irrigation may cause the soil humidity in some areas to be too high due to the topography. Therefore, this paper decided to design an intelligent constant-humidity irrigation system. This paper mainly designs a model for the consumption of water in the soil through the data collection of soil moisture. By using this model, an intelligent constant-humidity irrigation system is established. The main method is to calculate the next time the water supply should be based on the water consumption model in the soil. In addition to the previously collected values, the paper system can also store the humidity changes after each irrigation into the database. At the time of water supply, the data in the database will be searched first for calculation to achieve self-learning. This paper builds the soil humidity consumption model by collecting humidity data of soil. An intelligent constant-humidity irrigation system is implemented based on this model. The main idea is to predict the soil humidity according to the collected humidity data. A large number of different depths and different distances soil humidity are collected and then the regression function is applied to estimate the trend of humidity variation. The forecasting humidity is according to the regression function and is verified via the automatic irrigation system. Experimental results show that the prediction error of the humidity value at the irrigation interval of 2 hours is 1.07%, and the prediction error of the humidity value at the irrigation interval of 3 hours is 0.79%, and the prediction error of the humidity value at the irrigation interval of 4 hours is 0.47%. After combining the three-axis moving system, our method can also maintain the soil humidity in multiple areas with a specific range, and the maintenance time can reach 70% of the total experimental time.
Books on the topic "Irrigation intelligente"
Zhu, Xingye, Alexander Fordjour, Junping Liu, and Shouqi Yuan. Dynamic Fluidic Sprinkler and Intelligent Sprinkler Irrigation Technologies. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8319-1.
Full textDynamic Fluidic Sprinkler and Intelligent Sprinkler Irrigation Technologies. Springer, 2024.
Find full textWang, Banshun, Yaqi Xu, Hong Li, and Guangrong Zhang. Discussion on the Construction of Intelligent Irrigation Area. UK Scientific Publishing Limited, 2022.
Find full textZhu, Xingye, Alexander Fordjour, Shouqi Yuan, and Junping Liu. Dynamic Fluidic Sprinkler and Intelligent Sprinkler Irrigation Technologies. Springer, 2023.
Find full textBook chapters on the topic "Irrigation intelligente"
Kumar, Parveen, Balwant Raj, Sanjeev Kumar Sharma, and Balwinder Raj. "Intelligent Irrigation Systems." In AI for Big Data-Based Engineering Applications from Security Perspectives, 187–213. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003230113-8.
Full textFaridi, Hamideh, Babak Ghoreishi, and Hamidreza Faridi. "Intelligent Irrigation and Automation." In Handbook of Irrigation Hydrology and Management, 295–320. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9780429290152-19.
Full textLaabidi, K., M. Khayyat, and T. Almohamadi. "Smart grid irrigation." In Innovative and Intelligent Technology-Based Services for Smart Environments – Smart Sensing and Artificial Intelligence, 217–22. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003181545-31.
Full textDasgupta, Ajanta, Ayush Daruka, Abhiti Pandey, Avijit Bose, Subham Mukherjee, and Sumalya Saha. "Smart Irrigation: IOT-Based Irrigation Monitoring System." In Advances in Intelligent Systems and Computing, 395–403. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1544-2_32.
Full textAbhishek Kathpal, Nikhil Chawla, Nikhil Tyagi, and Rakshit Yadav. "Low Cost Intelligent Irrigation System." In Proceedings of the International Conference on Data Engineering and Communication Technology, 309–18. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1675-2_32.
Full textTalele, Ajay, Milind Rane, Omkar Jadhav, Mohit Burchunde, and Aniket Pardeshi. "Irrigation Using IOT Sensors." In Intelligent Computing and Networking, 281–92. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3177-4_20.
Full textAllali, Mahamed Abdelelmadjid, Kawther Nassima Addala, Nassima Ali Berroudja, Mounir Tahar Abbes, Zoulikha Mekkakia Maaza, Walid Kadri, and Abdelhak Benhamada. "New Monitoring Framework Intelligent Irrigation System." In Smart and Sustainable Agriculture, 166–85. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88259-4_12.
Full textSengupta, Yagnyasenee, Angelina Chiran, and Jasmin Gupta. "Smart Irrigation System Using IoT-Based Devices." In Embedded Artificial Intelligence, 208–26. Boca Raton: Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003481089-15.
Full textKoteswara Rao, M., M. Satish Kumar, M. Jaijaivenkataramana, and Ch Sai Sowjanya. "ESP32 Based Irrigation System." In Intelligent Cyber Physical Systems and Internet of Things, 465–74. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18497-0_35.
Full textAwawda, Jawad, and Isam Ishaq. "IoT Smart Irrigation System for Precision Agriculture." In Intelligent Sustainable Systems, 335–46. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7663-6_32.
Full textConference papers on the topic "Irrigation intelligente"
Al-Smadi, Adnan, Murar Al-Nusairat, Ammar Erjoub, Laith Freitekh, and Mohammed Khouj. "Intelligent Farm Irrigation and Safety System." In 2024 25th International Arab Conference on Information Technology (ACIT), 1–4. IEEE, 2024. https://doi.org/10.1109/acit62805.2024.10876975.
Full textSRIRAM, K. P., E. Anbalagan, S. Sasikumar, M. Guru Vimal Kumar, J. Paramesh, and Dr P. Kola Sujatha. "Intelligent Irrigation Mechanism to Enhance the Sensor Network Topology by Using Machine Learning and Artificial Intelligence." In 2024 Second International Conference on Advances in Information Technology (ICAIT), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690523.
Full textB, Ramya, Gunabalan P, and Sabithran D. "Automatic Irrigation Controller Using IoT." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI), 373–77. IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894411.
Full textLi, Yongheng, Rikun Wei, Huaning Song, Li Chen, Yajun Liang, Qingping Meng, and Shiye Liao. "The design of an intelligent irrigation system based on STM32." In Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), edited by Pierluigi Siano and Wenbing Zhao, 229. SPIE, 2024. http://dx.doi.org/10.1117/12.3034302.
Full textHuang, Wen-Lin, You-Rong Lin, and Quan-Yan Zeng. "Intelligent Control System for Water-Saving Irrigation of Greenhouse Plants." In 2024 IEEE 6th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), 193–96. IEEE, 2024. https://doi.org/10.1109/ecbios61468.2024.10883765.
Full textAlex, B., G. Jignasa, K. Madhubabu, and A. Gopi. "AI-Driven Smart Irrigation: Enhancing Agricultural Water Efficiency Through Intelligent Valve Regulation in Piped and Micro Irrigation Networks." In 2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT), 76–81. IEEE, 2024. http://dx.doi.org/10.1109/ic2sdt62152.2024.10696274.
Full textRiahi, Said, Hsen Abidi, Jamel Riahi, and Abdelkader Mami. "Innovative UV Water Treatment Solutions for Greenhouse Irrigation Using Intelligent MPPT." In 2024 IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE), 1–5. IEEE, 2024. https://doi.org/10.1109/icaige62696.2024.10776719.
Full textWang, Meng, Yilang Qin, Jie Zhang, Qing Zhao, Xiuying Han, Guoqiang Li, and Kai Wang. "Intelligent Water Saving Irrigation System Based on Moisture Sensors and Modelling." In 2024 3rd International Symposium on Sensor Technology and Control (ISSTC), 214–18. IEEE, 2024. https://doi.org/10.1109/isstc63573.2024.10824102.
Full textStephen, Vimal Kumar, Sanjiv Sharma, Zaina Rashid Salim Al-Jufaili, and Senthil Jayapal. "Implementing an Intelligent Irrigation System in Oman Using Machine Learning Technique." In 2025 3rd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT), 439–44. IEEE, 2025. https://doi.org/10.1109/dicct64131.2025.10986501.
Full textUpadhyaya, Animesh, Debdutta Pal, and Rajesh Dey. "EECSI: AN IOT APPLICATION-BASED ENERGY EFFICIENT THREE-LAYER CLUSTERED SMART IRRIGATION APPROACH." In TOPICS IN INTELLIGENT COMPUTING AND INDUSTRY DESIGN (ICID). Volkson Press, 2022. http://dx.doi.org/10.26480/icpesd.02.2022.141.145.
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