Journal articles: 'Irrigation intelligente'

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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.

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Bladder irrigation is one of the effective measures of prevention and control of urinary tract infection in patients with long-term indwelling catheter. Based on traditional artificial bladder irrigation technology, this paper presents a kind of intelligent bladder irrigator. The control system of this irrigator includes PLC and touch screen. The irrigator uses weighing sensor to monitor the weight of infusion bag in real time and controls pinch valve to switch the infusion tube. This paper has completed the design of the mechanical structure and control system. Intelligent bladder irrigator can realize the automation of traditional bladder irrigation.

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Villa 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.

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Implementing artificial intelligence in agriculture can improve efficiency, reduce pollution, and promote more effective agricultural production. Efficient irrigation management avoids wasting water and ensures that plants receive the right amount of water at the right time. The purpose of this research is to present an intelligent irrigation system based on neural networks and fuzzy logic, to avoid the presence of pests due to excess relative humidity in rose crops in Cotopaxi. A mixed methodology was used. The SCRUM methodology, Android Studio as an integrated development environment, a relational database management system and the Mobile-D method were used as software elements. For the prototype construction, the main hardware element that was used was the Arduino Board. The system for irrigating automated water using fuzzy logic took less time than manual irrigation. Training actions were proposed for employers and employees in the use and maintenance of the automated irrigation system, to maintain continuous improvement in the process

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Fedosov, 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.

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Abstract. The relevance of artificial intelligence in agriculture is substantiated for irrigation optimization. (Research purpose) To report on the progress made over the past few years in the application of artificial intelligence to optimize crop irrigation. (Materials and methods) The review focuses on the most salient facts and important scientific information on the application of artificial intelligence in crop production. The review is based on Various databases (Google Scholar, PubMed, Science Direct, SciFinder, Web of Science, RSCI) and online sources (Research Gate, Springer Nature Open Access, Wiley Online Library). It is shown how the integration of machine learning models can provide intelligent irrigation management. The review reports on the research trends and applicability of machine learning methods, as well as the deployment of developed machine learning models for sustainable irrigation management. (Results and discussion) Mobile and web platforms are shown to be able to facilitate intelligent irrigation management. Machine learning proves to be one of the central areas of artificial intelligence helping researchers to work more creatively and efficiently. The review notes the problems of introducing artificial intelligence in crop production and specifies the future research areas in the machine learning implementation and digital farming solutions. (Conclusions) The relevance of the intelligent system in irrigation and water management is proved for sustainable agriculture. It is revealed that, despite the extensive literature available, machine learning modeling for crop irrigation management is still in its infancy. The countries leading in this area are China, the United States and Australia.

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Jahanavi, 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.

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Gulomjonovich, 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.

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Smart irrigation systems integrate technology with agriculture to optimize water usage, reduce waste, and increase crop yields. This article explores the design of smart irrigation systems, discussing the essential components, methodologies, and technological innovations that enhance the efficiency and effectiveness of irrigation practices. The study identifies key challenges in system design and proposes solutions based on current trends in sensor technology, data analytics, and automation. The results of this investigation highlight the importance of interdisciplinary collaboration and innovative strategies to create robust, scalable, and sustainable irrigation solutions.

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Shaglouf, 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.

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The expansion of agriculture to provide the necessary food is related to the availability of water, but the limited availability of irrigation requires research on techniques to reduce water losses. This paper presents an application of a prototype design of microcontroller based on an intelligent irrigation system which will allow irrigation to take place in the areas. This method can be applied to the system of drip irrigation and its impact on the quantities of water used in irrigation as its application is part of the solution to the problem of water shortage suffered by Libya in addition to reducing the amount of water wasted while irrigating crops. In this study, a network of smart irrigation system was designed for a 5-hectare farm in AL-Sawawa area, located to the east, at about 20 km from Sirte city. The farm was divided into two parts, a vegetable crops section with an area of 3ha and the other section of 2 ha for olive trees. The intelligent irrigation system senses the moisture content of the soil and the temperature of the air through the sensors and turns on or off the water pumps using the relays to carry out this procedure. The main advantage of using this irrigation system is to minimize human intervention and ensure proper irrigation. The microcontroller serves as the main unit of the entire irrigation system, Photovoltaic cells are used to provide solar energy as an energy supply for the whole system. The system is controlled by the microcontroller; it obtains data from the sensors, it compares the data as pre-programmed, and the output signals activate the relays to operate the pumps to start the irrigation process.

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Arlanova, 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.

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The study investigates the application of artificial intelligence for optimizing irrigation systems in agriculture, aiming to reduce water losses and improve production efficiency. Traditional irrigation methods and their drawbacks, particularly in water-scarce regions, are analyzed. Existing approaches to using artificial intelligence in agricultural technologies for predicting water needs and regulating irrigation are examined. A mathematical model based on machine learning algorithms is developed to predict the optimal water volume required for irrigation of agricultural crops. Key factors affecting water consumption, such as temperature, soil moisture, and precipitation, are identified. The study finds that using the proposed model reduces water usage by 15% while maintaining stable crop yields. The results of testing the model on an experimental plot in Lebap region of Turkmenistan demonstrate its effectiveness in real conditions. It is substantiated that the implementation of such intelligent irrigation management systems can significantly improve the sustainability of agriculture in the face of climate change.

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Zhai, 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.

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Although the irrigation technologies based on the Decision-making System (DMS) began in the late 1990s, while being merely embryonic from laboratory research into application in the agricultural irrigation areas, DMS based on intelligent algorithms have drawn much attention from the academia over the recent years. In this study, we have provided an overview of the decision-making technology based on knowledge engineering for intelligent irrigation system referred to as Knowledge-based Engineering (KBE). As the modern technical research and scientific theory on agricultural water saving is further developed, the water-fertilizer irrigation is becoming increasingly intelligent. We have put forward the concept of KBE intelligent irrigation system and its support to decision-making in the study, while adopting the techniques and methods of knowledge engineering. In addition, we have combined our research findings with the expert knowledge on the water-fertilizer irrigation in a system integrated with computer network, intelligent reasoning and artificial intelligence (AI), among other modern high-techs. We have set up the decision-making models and analytical methods of irrigation and fertilization for KBE by referring to the expert experience and data of fertilization. Moreover, we have taken into account the web crawler technology in irrigation and fertilization, and we have put forward novel methods of knowledge acquisition based on the web crawler. Correspondingly, we have established the knowledge base for the decision-making support system tailored to irrigation and fertilization. The experiment result shows that the recommended irrigation quota is compared with local cultivation technology experience to obtain a decision accuracy of 81.7%. And the water and fertilizer management plan obtained by the intelligent decision-making system has a thicker stem and higher plant height during the growth period than the crops obtained by local cultivation experience. The output of the decision-making system is 620 kg, which a relative increase of 5.08% is compared with the 590 kg obtained from local cultivation experience.

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Loubna, 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.

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K. 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.

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Solar radiation is a critical factor influencing agricultural productivity and water resource management. Irrigation systems play a pivotal role in maintaining crop health and yield, and optimizing their operation requires accurate solar radiation data. This abstract explores the significance of solar radiation data in enhancing the efficiency of irrigation systems. Irrigating agricultural fields using an intelligent information system plays a crucial role. This study introduces an irrigation control system employing closed-loop control to use the available water resources efficiently. Continuous data collection from field sensors was done and transmitted to a central station in wireless mode. The data was then retrieved and processed in a computer-based or microcontroller-based solution model, enhancing system autonomy, reliability, and cost-effectiveness. Weather conditions are translated into fuzzy set values. The pump, water outlet valves, and sprinklers are set into motion according to control commands. This research simplifies the need for manual labor and reduces water wastage.

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Deshpande, Swati, Vinaya Kavalgi, Sunita Biradar, and Suvarna Nandyal. "Intelligent Irrigation System." International Journal of Computer Applications 167, no. 14 (June 22, 2017): 26–29. http://dx.doi.org/10.5120/ijca2017914365.

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Jha, Kirtan, Aalap Doshi, and Poojan Patel. "INTELLIGENT IRRIGATION SYSTEM USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING: A COMPREHENSIVE REVIEW." International Journal of Advanced Research 6, no. 10 (September 30, 2018): 1493–502. http://dx.doi.org/10.21474/ijar01/7959.

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Jayaraman, Gowrishankar, Vaishali Singh, Kuldeep Singh Kulhar, and Zuleika Homavazir. "Unravelling the potential of Big Data-driven decision-making in sustainable water irrigation: An AI perspective." Multidisciplinary Reviews 6 (April 28, 2024): 2023ss069. http://dx.doi.org/10.31893/multirev.2023ss069.

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Big Data-driven initiatives have transformed several industries by using large databases to extract vital information and enhance operations. In the field of sustainable water irrigation, Big Data enables intelligent water management, allowing for exact resource allocation, monitoring and analysis. This combination enables smart decision-making, increasing productivity in agriculture while reducing water consumption and environmental impacts. The revolutionary influence of data-driven approaches on managing water resources for sustainable and efficient irrigation practices has significant potential. The research highlights the importance of Big Data-driven approaches in water management by examining their applicability. It explores the artificial intelligence (AI) techniques for water irrigation that is sustainable and determines significant affecting factors efficient water management. The study encourages AI in intelligent water management systems and explains the application of big data in irrigation. It illustrates how smart water management which includes hydrated irrigation and predictive water infrastructure can contribute to AI-driven water management. To handle complicated water supply problems, the research presents an estimated water level model, highlighting the integration of AI and indicating the revolutionary potential of these techniques in attaining sustainable water irrigation. We highlight the novel possibilities of big data-driven methods for sustainable water irrigation, illustrating the essential part of AI techniques in effective water resource allocation.

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Christias, Panagiotis, Ioannis N. Daliakopoulos, Thrassyvoulos Manios, and Mariana Mocanu. "Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support." Mathematics 8, no. 5 (May 3, 2020): 717. http://dx.doi.org/10.3390/math8050717.

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This paper explores methodologies for developing intelligent automated decision systems for complex processes that contain uncertainties, thus requiring computational intelligence. Irrigation decision support systems (IDSS) promise to increase water efficiency while sustaining crop yields. Here, we explored methodologies for developing intelligent IDSS that exploit statistical, measured, and simulated data. A simple and a fuzzy multicriteria approach as well as a Decision Tree based system were analyzed. The methodologies were applied in a sample of olive tree farms of Heraklion in the island of Crete, Greece, where water resources are scarce and crop management is generally empirical. The objective is to support decision for optimal financial profit through high yield while conserving water resources through optimal irrigation schemes under various (or uncertain) intrinsic and extrinsic conditions. Crop irrigation requirements are modelled using the FAO-56 equation. The results demonstrate that the decision support based on probabilistic and fuzzy approaches point to strategies with low amounts and careful distributed water irrigation strategies. The decision tree shows that decision can be optimized by examining coexisting factors. We conclude that irrigation-based decisions can be highly assisted by methods such as decision trees given the right choice of attributes while keeping focus on the financial balance between cost and revenue.

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Liu, Chengwen, Guoliang Liu, and Haitao Zhang. "Optimized Design for Reliability of Pointer Irrigation Machine Components for Intelligent Computing." International Transactions on Electrical Energy Systems 2022 (September 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/7114934.

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The application of intelligent technology has realized the transformation of people’s production and lifestyle, and also promoted the development and transformation of the agricultural field. At present, the application of agricultural intelligence is getting stronger and stronger; using its intelligent advanced methods and technologies, this paper aimed to achieve the optimization of sprinkler irrigation machine parts in the intelligent network environment to promote the rapid development of agriculture, and proposed the use of the NSGA-II algorithm in intelligent computing to guide the integration of artificial intelligence and pointer sprinkler parts, which helps to analyze and solve the objective problem of machine failure and parts damage in agriculture. In the study of the sprinkler gear system, from the perspective of gear efficiency, since it is optimized according to the minimum efficiency point of the fourth gear of the gear reducer, compared with the gear efficiency of 49.05% before this point, the efficiency of this point after optimization is 59.45%, and the minimum efficiency point will be increased by 21.2%. And because the energy loss unrelated to the power loss load will be greatly reduced, these energy losses have a greater relationship with the structure of the gearbox. In terms of each gear, compared with the previous period, the efficiency of the first gear was increased by 8.5% to 15.9%; the efficiency of the second gear increased by 8.7% to 17.4%; the efficiency of the third gear increased by 9.4% to 18.7%; and the efficiency of the fourth gear increased by 10.1% to 21.2%. Therefore, it is currently necessary to optimize the components of the sprinkler irrigation machine.

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Gosavi, Nidhi, and Deepa Chourasiya. "SMART IRRIGATION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (March 13, 2024): 1–9. http://dx.doi.org/10.55041/ijsrem29118.

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Water scarcity is currently one of the biggest issues facing the globe, and agriculture is an intense activity that uses a lot of water. Therefore, a system that makes prudent use of water is needed. About 70% of people in India are employed in agriculture, making it an agricultural nation. With the use of technology, it can be enhanced. An autonomous watering system can help with irrigation management. It suggests irrigating the agricultural lands automatically. At the moment, automation plays a significant role in human existence. This paper addresses and reviews intelligent irrigation ways. In addition to being comfortable, it also saves time, energy, and improves efficiency.

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Pantha, Deepak, and Dr Roshan Koju. "Evaluating the use of Intelligent Irrigation Systems Based on the IoT in Winter Season of Vyas Municipality Ward No-1 and 13 of Nepal." International Journal of Agriculture and Animal Production, no. 36 (October 5, 2023): 32–46. http://dx.doi.org/10.55529/ijaap.36.32.46.

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Nepal's agricultural progress seems insufficient, particularly in its hilly regions where inadequate cultivation has led to a trend of people seeking employment overseas. This study investigates the contemporary agricultural landscape, focusing on irrigation management. Despite irrigation not covering all hilly areas, even in irrigated arable lands, efficient water management remains a challenge. Traditional methods result in water wastage, while modern techniques optimize water use, preventing agricultural issues caused by excess water. This research delves into the potential, difficulties, and prospects of IoTbased irrigation in Nepal's hilly regions. The primary aim is to assess the reality of IoTdriven irrigation in Nepal's hilly regions. The primary aim is to assess the reality of IoTdriven irrigation in the hilly area Vyas Municipality's ward no-1 and 13. Over a six-month test perod, around 95% of water wastage was curbed through IoT irrigation, as compared to manual methods. Pilot test demonstrated the positive impact of reduce water wastage on agriculture. Additionally, a 90-day comparative study of plant growth, weight, and development was conducted. analysis of challenges, benefits, and investments associated with modern irrigation technology in the hilly areas revealed IoT-based irrigation's significant potential and suitability. This research underscores its pivotal role in modernizing agriculture, enhancing farmer productivity, and stimulating higher yields.

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Chand, Jeet, Rupesh Acharya, Roshan Pandey, Milan Paudel, and Sanjeeb Bimali. "Automation in drip irrigation system: A comprehensive review with mathematical modeling and optimization algorithms." International Journal of Sustainable Agricultural Research 12, no. 1 (April 15, 2025): 67–80. https://doi.org/10.18488/ijsar.v12i1.4166.

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This study aims to review and document literature related to drip irrigation and its advancement towards automation in agriculture so that farmers can benefit from the optimum use of input resources, primarily water and fertilizer. Additionally, this review examines technological improvements such as sensor integration, wireless connectivity, and systems because the Internet of Things, microcontrollers, artificial intelligence, and real-time monitoring are critical tools for enhancing agricultural productivity and environmental sustainability. Mathematical models and formulations related to intelligent drip systems were reviewed, along with a deeper exploration of optimization algorithms employed, especially in terms of improving irrigation efficiency, resource optimization, and system performance. Furthermore, a critical analysis was undertaken in a comprehensive explanation of the system design, including real-world applications, with clear mathematical formulations and optimization models. This study found that among different irrigation methods, an intelligent drip system has the highest application efficiency, distribution uniformity, better crop yields, and input resource savings. Also, this study postulates that drip automation allows for accurate water and nutrient distribution, reducing fertilizer runoff and environmental harm. In contrast, drip irrigation has been found to be characterized by higher capital costs and the need for skilled personnel to manage the system, which are, however, equalized by higher yields and savings of production inputs. Reviewed literature indicated that high-valued cash crops are most appropriate for drip automation and suggest extensive application of automated drip systems for environmental sustainability in agriculture. This study recommends further research to make drip irrigation cost-effective, intelligent, and more farmer-friendly.

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Ahmed, Zeeshan, Dongwei Gui, Ghulam Murtaza, Liu Yunfei, and Sikandar Ali. "An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands." Agronomy 13, no. 8 (August 11, 2023): 2113. http://dx.doi.org/10.3390/agronomy13082113.

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Global drylands, covering about 41% of Earth’s surface and inhabited by 38% of the world’s population, are facing the stark challenges of water scarcity, low water productivity, and food insecurity. This paper highlights the major constraints to agricultural productivity, traditional irrigation scheduling methods, and associated challenges, efforts, and progress to enhance water use efficiency (WUE), conserve water, and guarantee food security by overviewing different smart irrigation approaches. Widely used traditional irrigation scheduling methods (based on weather, plant, and soil moisture conditions) usually lack important information needed for precise irrigation, which leads to over- or under-irrigation of fields. On the other hand, by using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind.

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Liu, Zhigang, and Qinchao Xu. "An Automatic Irrigation Control System for Soilless Culture of Lettuce." Water 10, no. 11 (November 20, 2018): 1692. http://dx.doi.org/10.3390/w10111692.

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To achieve precise measurement of moisture content in the substrate and intelligent water-saving irrigation, a simple and low cost automatic irrigation control system based on ZigBee wireless network has been developed. A software with irrigation strategy was proposed based on the models of substrate wetting pattern, lettuce root zone and the evapotranspiration. The system could detect substrate moisture in real-time and irrigate automatically according to the threshold of substrate and the irrigation strategy. The average fresh weight per plant under intelligent irrigation are 16.60% and 11.37% higher than manual control irrigation at least in different growth stages in spring and summer, the average drainage rate of intelligent irrigation is 16.08% and 17.06% smaller than manual control irrigation in spring and summer, and the irrigation water use efficiency of intelligent irrigation is 68.03% and 98.61% higher than manual control irrigation in spring and summer. The results show that the system is a promising tool for scientific and rational irrigation decision.

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Choudhary, Savita, Vipul Gaurav, Abhijeet Singh, and Susmit Agarwal. "Autonomous Crop Irrigation System using Artificial Intelligence." International Journal of Engineering and Advanced Technology 8, no. 5s (June 29, 2019): 46–51. http://dx.doi.org/10.35940/ijeat.e1010.0585s19.

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Agriculture plays a significant role in the economy and its contribution is based on measurable crop yield which is highly dependent upon irrigation. In a country like India, where agriculture is largely based on the unorganized sector, irrigation techniques and patterns followed are inefficient and often lead to unnecessary wastage of water. This calls for the need of a system which can provide an efficient and deployable solution. In this paper, we provide an Automatic Irrigation System based on Artificial Intelligence and Internet of Things, which can autonomously irrigate fields using soil moisture data. The system is based on prediction algorithms which make use of historic weather data to identify and predict rainfall patterns and climate changes; thereby creating an intelligent system which irrigates the crop fields selectively only when required as per the weather and real-time soil moisture conditions. The system has been tested in a controlled environment with an 80 percent accuracy, thus providing an efficient solution to the problem.

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Yu, Song, Chang Lin Ao, and Hong Guang Chen. "Intelligent Irrigation System Reliability Simulation." Advanced Materials Research 544 (June 2012): 176–81. http://dx.doi.org/10.4028/www.scientific.net/amr.544.176.

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This paper presents the composition and working principle of a intelligent irrigation system, and studies the reliability of system. It analyzes the system's failure modes. It establishes the system simulation model by using fault tree analysis, and it uses Monte Carlo method to carry on reliability simulation. The simulation results are given. Some suggestions on improving and perfecting the system are put forward based on the results. The conclusions provide a reference for reliability and maintainability of intelligent irrigation system. It is of great significance to increase agricultural production.

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Pardeshi, Vidya D., Hemant T. Ingale, Shafique Ansari, Anil D. Vishwakarma, and Rajendra V. Patil. "Literature Survey of Intelligent Irrigation." International Journal of Innovations in Engineering and Science 9, no. 4 (August 5, 2024): 71–75. http://dx.doi.org/10.46335/ijies.2024.9.4.14.

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George Michael, T., Mardi Turnip, Erni Muniarti, Erwin Sitompul, and Arjon Turnip. "Development of an Irrigation System for Predicting Watering Time with ANFIS Method for Chili Plants." IOP Conference Series: Earth and Environmental Science 1083, no. 1 (September 1, 2022): 012081. http://dx.doi.org/10.1088/1755-1315/1083/1/012081.

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Abstract Recently, precision farming has become a necessity due to the increasing global demand for staples and water. Thus, farmers will need the availability of sufficient water and fertile soil to meet these needs. Due to the limited availability of both resources, farmers need solutions that change conventional farming systems. Precision farming is the solution to deliver larger and more profitable yields with fewer resources. Currently, several artificial intelligence-based irrigation models have been proposed to use water more efficiently. However, the limited irrigation capabilities of the previous model make it unsuitable for unpredictable climates. The authors conducted research on ANFIS-based intelligent irrigation systems for irrigation system models and the Internet of Things (IoT) to connect sensors to actuators via the cloud. The daily water requirement parameter for plants can be determined using conventional measurements (Gravimetry), this parameter will be the output parameter in the ANFIS modeling. This modeling is compared with reference measurements (conventional) resulting in a fairly accurate accuracy of 87.5%. The proposed system is simple and affordable which makes the technology more precise.

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ALSAADOUN, Sara, and Fairouz TCHIER. "Proposed Intelligent Irrigation System for Riyadh City Using Fuzzy Logic." Eurasia Proceedings of Science Technology Engineering and Mathematics 28 (August 15, 2024): 397–407. http://dx.doi.org/10.55549/epstem.1523614.

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Considering the insufficient rainfall and rapid evaporation of soil moisture in desert regions, irrigation becomes very challenging in Riyadh. Automation is therefore required for optimum productivity and water conservation. According to the vision of the Green Riyadh project, recycled water will be used in the irrigation system to irrigate all green lands. The method of irrigation and the quantity of water being used must both be considered. Since drought inhibits plant growth and excessive moisture limits plants' ability to absorb nutrients and raises the possibility of disease development, only the amount of water needed by plants should be used. Artificial intelligence and fuzzy logic are increasingly seen as solutions to be implemented in smart drip irrigation systems. The fuzzy logic control system will aid in water conservation given its shortage, which has become a major worldwide concern. The system also has the advantage of conserving moisture to counteract inadequate rainfall, saving electricity and reducing labor costs. For such advantages, an intelligent irrigation control system based on fuzzy logic adapted to the climate of Riyadh will be proposed. Based on the measured soil moisture, ambient temperature, air humidity and solar irradiance, the fuzzy controller calculates the necessary irrigation duration. The features of the fuzzy logic toolbox in MATLAB are used to create the proposed system.

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Zeng, Jiefeng, Peiling Yang, Weijie Liu, and Xudong Xiang. "Influence of Fractal Disc Filter Flow Channel Parameters on Filtration Performance." Applied Sciences 14, no. 17 (August 25, 2024): 7505. http://dx.doi.org/10.3390/app14177505.

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The research and development of a new disc filter is a key link in intelligent irrigation systems, the core of efficient and water-saving irrigation development, and also an important joint effort to ensure a clean water source in micro-irrigation systems. In this paper, the independent research and development of the fractal flow passage disc filter was taken as the research object, and the disc filter numerical simulation cell (FLUENT) and artificial intelligence technology (Back Propagation Neural Network) were combined to optimize the filter flow channel parameters, including the tilt angle, the length and height of the bottom of the internal section triangle, the taper, the position and number of buffer slots, etc. A new type of disc filter with lower head loss, larger flow capacity, higher filtration efficiency, and longer running time is proposed. It has certain reference value and promotion significance for the future development and design of high-performance disc filters and their wide use.

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R. Durga Bhavani, E.Sai Jaya Sri, P. Nagaraju, Shaik Anish, Dr Sumanth Kumar Panguluri, and Shaik Akbar Vali. "Intelligent water management system Using IoT." international journal of engineering technology and management sciences 9, no. 2 (2025): 433–42. https://doi.org/10.46647/ijetms.2025.v09i02.054.

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Irrigation systems are crucial for agriculture, yet improper water distribution frequently results in wastage and increased water usage. This project demonstrates an Intelligent Water Management System using Internet of Things (IoT) that automates and optimizes water distribution for irrigation, resulting in more efficient water usage. The system is based on the Arduino UNO platform and includes essential components like a soil moisture sensor, servomotors, an LCD display, and an ESP8266 Wi-Fi module. The system has two modes of operation: Auto Mode, in which the system automatically measures soil moisture independently and regulates water supply to plants according to real-time measurements, and Manual Mode, in which users can monitor and operate the irrigation system remotely through a mobile app or web portal via the Wi-Fi module. The LCD shows the soil moisture and status of the system in real time, enabling users to track the performance of the irrigation system. The technology will minimize water loss, regulate irrigation time, and enable sustainable agriculture through automation of the irrigation process to save water more efficiently.

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Callejas-Rodríguez, Rodrigo, and Oscar Seguel. "Paquete tecnológico UchileCrea para el control inteligente del riego en sistemas frutícolas." Aqua-LAC 13, no. 1 (March 31, 2021): 128–42. http://dx.doi.org/10.29104/phi-aqualac/2021-v13-1-09.

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El manejo del riego en la fruticultura es una de las principales labores agrícolas que deben ejecutar los productores en sus predios. A pesar de existir, teóricamente, un sinnúmero de alternativas para su control, ellas no son necesariamente eficientes en determinar en forma clara el tiempo y la frecuencia de riego durante la temporada de producción. Por esta razón, el equipo UchileCrea de la Universidad de Chile ha trabajado por más 20 años para definir y validar un paquete tecnológico (PT) para el control del riego, enmarcado en la integración de nuevas tecnologías (sondas de capacitancia, IoT, manejo de precisión, NDVI, etc.) y que en la actualidad se hace más necesario, por el recurrente incremento de la escasez hídrica que vive el país producto del cambio climático, lo que obliga a aumentar la eficiencia de uso del agua de riego (EUAr). De acuerdo a la metodología internacional establecida para la generación de paquetes tecnológicos, se definieron 4 etapas que se debían cumplir. Etapa 1: selección de tecnologías y conformación de sub-paquetes. Etapa 2: propuesta de paquete tecnológico. Etapa 3: validación y retroalimentación. Etapa 4: promoción y adopción del PT. Se pudo verificar que es posible definir el tiempo y frecuencia de riego en forma óptima, mejorando los rendimientos, ahorrando entre 20 a 40% del recurso hídrico utilizado, aumentando la EUAr y disminuyendo el consumo de energía eléctrica. Adicionalmente, este PT permite tener una nueva herramienta probada para enfrentar la escasez hídrica que vive el país y, a través de la promoción permanente, seguir incorporándolo en el sistema frutícola nacional.

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Li, Kang, Lin Ding, and Shun Li. "Development of Mobile Intelligent Sprinkler Irrigation Device Based on IOT." Journal of Education, Teaching and Social Studies 4, no. 2 (June 27, 2022): p45. http://dx.doi.org/10.22158/jetss.v4n2p45.

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Sprinkler irrigation is a major aspect of water use. At present, most of the domestic sprinkler irrigation for green lawns and flowerbeds adopts the rocker-arm rotary sprinkler that is common in the market. Aiming at the problem that the rocker arm rotary sprinkler is not intelligent, it is not suitable for areas that require intelligent irrigation, and cannot intelligently manage the sprinkler irrigation area, I understand the principles of various sprinkler irrigation devices on the market, and propose to automatically adjust the sprinkler range to make sprinkler irrigation intelligent and efficient. Precision to adapt to various types of sprinkler irrigation sites. And it is used for lawn or flowerbed irrigation, and can move freely in the irrigation area without being fixed on the ground. Compared with the existing rocker arm rotary sprinkler, it reduces the initial installation cost and post-maintenance cost.

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KAMYSHOVA, G. N. "MODELING OF NEURAL PREDICTIVE CONTROL OF IRRIGATION MACHINES." Prirodoobustrojstvo, no. 1 (2021): 14–22. http://dx.doi.org/10.26897/1997-6011-2021-1-14-22.

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The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.

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Hu, Xiaofei, Xia Sun, Qinghong Li, Qianqian He, and Yajun Li. "Design and implementation of intelligent irrigation system." E3S Web of Conferences 260 (2021): 03010. http://dx.doi.org/10.1051/e3sconf/202126003010.

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In view of the problems existing in traditional irrigation, such as high time cost, poor reliability, waste of water resources. The intelligent irrigation system based on STM32 and BC95 is designed and implemented. The soil information is received through temperature sensor and humidity sensor, which is sent from the sampling node to the remote terminal serial port. The controller sends the signal to the output end for intelligent irrigation. The practice shows that the wireless communication mode of data transmission using STM32 and NB-IoT (narrow band-internet of things) technology can meet the requirements of reducing the time cost and enhancing the reliability of the system, and can meet the goal of data transmission of intelligent irrigation system and water-saving irrigation. it can be seen that the soil moisture data in the figure significantly changes.

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Yao, Kam Yi Eason, Eric Youzhen Wang, Xinyi Lei, and Boyang Xu. "Industrial Applications of Artificial Intelligence Technologies and Their Socio-economic Impacts." Advances in Economics and Management Research 11, no. 1 (July 17, 2024): 184. http://dx.doi.org/10.56028/aemr.11.1.184.2024.

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This paper outlines the development of Artificial Intelligence (AI) technology and its contribution to the Industrial Revolution. From the First Industrial Revolution to the Fourth Industrial Revolution, technological innovations have continuously promoted productivity and social progress. In particular, the fourth industrial revolution, represented by AI technology, has penetrated into various industries and fields with its wide range of applications, significantly improving production efficiency and quality. In the manufacturing industry, AI technology realizes automated production and intelligent quality inspection, reducing labor costs and improving product quality. In the field of agriculture, AI technology has effectively improved the yield and quality of crops through drone spraying and intelligent irrigation. In addition, AI technology also plays an important role in social governance, improving the efficiency and level of social management.

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Li, Hongyuan, Guizhang Zhao, Simin Xie, Lingying Kong, Hongliang Li, and Hepeng Zhang. "The Mechanism of the Influence of Different Irrigation Methods on Groundwater Recharge." Water 16, no. 11 (May 30, 2024): 1565. http://dx.doi.org/10.3390/w16111565.

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In the Yinchuan Plain, the main source of water supply for agricultural crops is irrigation infiltration. Therefore, the irrigation process, method, and time are crucial for the rational planning and utilization of water resources in the region. In this study, the effects of different irrigation methods on groundwater recharge were investigated through irrigation quadrat tests combined with numerical simulations. The water content at depths of 10–50 cm had a more significant response to irrigation than that at 80 and 100 cm in the intelligent irrigation quadrat. The water content change at depths of 10–50 cm was smaller than that at 80 and 100 cm in the flood irrigation quadrat. The flood irrigation method had a greater impact on the water content in the deep vadose zone. The water content of intelligent irrigation was concentrated at depths of 30–50 cm, with weak groundwater recharge. The water content of the flood irrigation quadrat was concentrated at depths of 50–80 cm, with a significant impact in the vertical direction. The simulation results indicated that flood irrigation had the best effect on groundwater recharge, with an infiltration recharge coefficient of 0.73, compared to intelligent irrigation, which had an infiltration recharge coefficient of 0.41. When the groundwater depth range was 0.65–3.8 m, the infiltration recharge efficiencies of intelligent and flood irrigation were the highest at groundwater depths of 1.3 and 1.8 m. Our findings provide a scientific basis for methods of rational irrigation, which could help save water resources in the study area.

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Reshma Ghani, Md Owais Quadri, Mahmood farmaan, Md Anas, Praveen Kumar,. "Smart Irrigation and Crop Health Prediction." Proceeding International Conference on Science and Engineering 11, no. 1 (February 18, 2023): 1623–36. http://dx.doi.org/10.52783/cienceng.v11i1.314.

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The scarcity of clean water resources around the globe has generated a need for their optimum utilization Internet of Things (IoT) solutions, based on the application specific sensors’ data acquisition and intelligent processing are bridging the gaps between the cyber and physical world. IoT based smart irrigation systems can help in achieving optimum water-resource utilization in the precision farming landscape. This paper presents an open-source technology based smart system to predict the irrigation requirements of a field using the sensing of ground parameter like soil moisture soil temperature, and environmental conditions along with the weather forecast data from the Internet. The intelligence of the proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web-services and a web-based information visualization and decision support system provides the real-time information-insights based on the analysis of sensors data and weather forecast data. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging.

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Huang, Yu Kun. "Design of Agricultural Intelligent Irrigation System in Green Environment." Applied Mechanics and Materials 340 (July 2013): 1007–11. http://dx.doi.org/10.4028/www.scientific.net/amm.340.1007.

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The intelligent irrigation system can achieve precision irrigation, which is an effective way for the sustainable development of agriculture in arid area. In this paper, using the CC2430, intelligent irrigation system was designed and implemented according to the actual needs of the decision-making and management of plant irrigation. Distribution of soil temperature and humidity monitoring the system to solve the difficult and critical hardware products import prices too high and difficult to promote. The system cost compared to similar foreign products decreased 44.8%. Compared with traditional irrigation methods, crop water use efficiency of 22.6%

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Chunyao Huang, Yan Lu, and Hailiang Du. "An Intelligent Water-Saving Irrigation System." Journal of Water Chemistry and Technology 42, no. 6 (November 2020): 480–84. http://dx.doi.org/10.3103/s1063455x20060041.

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Pandao, Pratiksha Pradip, Abhi Rathi, and Prince Patel. "Smart Irrigation System Using Intelligent Robotics." European Journal of Information Technologies and Computer Science 1, no. 5 (November 16, 2021): 6–10. http://dx.doi.org/10.24018/compute.2021.1.5.26.

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To optimize water use for agricultural crops while also verifying water scarcity in the field, an automated irrigation system was developed. Weed management and control are critical for high-yielding, high-quality crops, and developments in weed control technologies have had a significant impact on agricultural output. Any weed control method that is effective must be both durable and versatile. Despite the variety in field circumstances, robust weed control technologies will successfully manage weeds. Weed control technology that is adaptable can change its strategy in response to changing weed populations, genetics, and environmental conditions. The system includes a distributed wireless network of soil-moisture and temperature sensors, as well as conductive sensors in the plant's root zone. Agate way unit also manages sensor data, triggers actuators, and sends data to an Android mobile device. To control water quantity, an algorithm with temperature and soil moisture threshold values was developed and programmed into a microcontroller-based gateway. The added future of this research is that we are utilising a robot to monitor the condition of the crop to see if it is affected by insects or not. The robot will move around the field, and we will be able to track the crop's health on our device.

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Jain, Abhinav. "INTELLIGENT AUTOMATED IOT BASED IRRIGATION SYSTEM." International Journal of Advanced Research in Computer Science 9, no. 2 (February 20, 2018): 512–15. http://dx.doi.org/10.26483/ijarcs.v9i2.5769.

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Elshaikh, Ahmed, Elsiddig Elsheikh, and Jamal Mabrouki. "Applications of Artificial Intelligence in Precision Irrigation." Journal of Environmental & Earth Sciences 6, no. 2 (July 16, 2024): 176–86. http://dx.doi.org/10.30564/jees.v6i2.6679.

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This paper provides an overview of the various applications of Artificial Intelligence (AI) in precision irrigation. It covers key research areas, methodologies, challenges, and future prospects in the field. The methodology is based on exploring how AI technologies are being used to optimize water management in agriculture and examines the growing body of research on the application of AI in irrigation systems. Deep investigation was conducted to explore how AI technologies can enhance water management in agriculture, leading to improved water management and crop yield in addition to resource efficiency. The paper discusses AI-based methods for monitoring soil conditions, weather forecasting, and real-time decision-making in irrigation. However, integration of AI systems with existing irrigation infrastructure and farming practices can be challenging, requiring significant investment in hardware and software.

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Zhang, Qian, Yu Bin Zhang, and Huo Mei Zhu. "Intelligent Irrigation Remote Control System Based on Internet of Things." Advanced Materials Research 955-959 (June 2014): 3404–7. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.3404.

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The paper presents an intelligent irrigation remote control system based on Internet of things to improve the calculation accuracy in water-saving irrigation. A set of fully functional website remote irrigation control system, SMS remote automatic irrigation control system, and the Android platform developed based on the mobile phone Java automatic irrigation remote control mobile client are designed, as well the precise irrigation water control machine and the remote control function are develop. It can realize the field of real-time information acquisition, monitoring, irrigation schemes, precise control machine remote monitoring and water at the scene of the greenhouse temperature and humidity regulating function through the browser, telephone, SMS, fetion and mobile phone client. Its practical application in the agricultural demonstration fields shows that this system is suitable for large acreage fields with its stability in running, convenience in operation and low cost in use.

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Huang, Shan, and Feng Mei Liang. "The Design and Implementation of Intelligent Farmland Irrigation System." Applied Mechanics and Materials 475-476 (December 2013): 782–86. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.782.

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Aiming at the problems of low efficiency of traditional irrigation systems as well as the pay difficulties, a new design scheme of intelligent irrigation system based on RFID is proposed. GPRS module, RFID module, the power measurement module being controlled through Master module completes the intelligent irrigation. The presented method shows convenience, stabilization, reliability and application value.

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42

P, Nandana, and Srikanth K. "AI Powered Irrigation." Recent Research Reviews Journal 3, no. 2 (December 2024): 424–38. https://doi.org/10.36548/rrrj.2024.2.009.

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The integration of artificial intelligence in irrigation is transforming contemporary agriculture by optimizing crop yields, conserving water resources, and promoting sustainable farming practices. Traditional irrigation systems often lead to water inefficiencies and misdistribution, which can harm the environment. AI-driven solutions, including machine learning algorithms and Internet of Things (IoT) sensors, enable real-time monitoring of soil moisture levels, weather changes, and crop requirements, ensuring that water is delivered precisely when and where it is needed. By adapting irrigation strategies to reflect evolving environmental conditions, this technology reduces water usage while enhancing both crop health and overall productivity. AI-enabled irrigation presents a scalable solution to address the increasing global demand for food and the challenges posed by water scarcity. Additionally, the use of data from drones and satellites further enhances the effectiveness of this innovative approach. This study presents a brief overview of the use of artificial intelligence in the irrigation system.

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Jia, Weibing, and Zhengying Wei. "Raspberry Pi-Embedded Intelligent Control System for Irrigation and Fertilization Based on deep learning." Journal of Physics: Conference Series 2504, no. 1 (May 1, 2023): 012034. http://dx.doi.org/10.1088/1742-6596/2504/1/012034.

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Abstract More than two-thirds of freshwater consumed worldwide are used for irrigation, and the existing control system for irrigation and fertilization is not intelligent and efficient enough. It is necessary to improve the utilization efficiency of irrigation water and fertilizer, and the intelligent level of irrigation and fertilization system. We have built tomato fertilizer deficiency and fertilization decision model based on leaf images and deep learning, this paper develops an Raspberry Pi-embedded intelligent control system for irrigation and fertilization based on previous deep learning models. The system included sensors, image acquisition, control part, control panel and remote control, the type of main hardware and sensors were selected, and the circuit of the system were designed. The C language is used to compile the source code of the deep learning framework, and the Python language is used to compress the deep learning model. The result shows that the compression rate of the deep learning model can reach 36.7% and the decision-making time can be shortened by 36% under the condition of ensuring the model accuracy. The Raspberry Pi-embedded intelligent control system can help farmers improve the utilization efficiency of irrigation and fertilizer.

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Wang, Fu Ping, and Si Fa Ding. "Zig Bee-Based Wireless Sensor Network Design of Irrigation Systems." Advanced Materials Research 850-851 (December 2013): 584–87. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.584.

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For irrigated areas still follow the traditional irrigation, causing serious water wastage. In this paper, chip CC2530 Zig Bee wireless network as the core, development and design coordinator nodes, routing nodes and devices terminal node, complete the irrigated areas of intelligent water-saving irrigation control system for wireless sensor networks networking structures. Equipment terminal node is responsible for collecting soil moisture data, the data is sent directly through the route or to the coordinator node, the coordinator node for data aggregation and passed to the intelligent controller, through the intelligent controller to make an accurate judgment, the implementation of efficient water-saving intelligent irrigation.

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Wan, Haicheng, Shanping Wang, Qifan Dong, and Hongyu Jia. "Intelligent Irrigation System for Agricultural Greenhouse Adaptive to Crop Growth Law." Journal of Electronic Research and Application 9, no. 1 (February 12, 2025): 113–20. https://doi.org/10.26689/jera.v9i1.9441.

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Greenhouse planting is a key method for increasing the yield of agricultural products in China. The Academy of Agricultural Sciences has conducted extensive research on the water requirements of greenhouse crops during various growth stages. Studies indicate that crops in the germination stage, seedling stage, and other stages of their growth cycle have different water needs. Proper irrigation can significantly enhance both crop quality and yield. To apply the Academy of Agricultural Sciences’ expertise on irrigation during different growth stages to practical farming, and to avoid improper irrigation at specific stages that could reduce crop production and quality, our team has designed an intelligent irrigation system for agricultural greenhouses. This system adapts to the growth patterns of crops by establishing an irrigation model based on characteristic images of each growth stage and irrigation data provided by the Academy. Using image recognition technology, the system accurately identifies the growth stage of crops. It then employs a pre-set irrigation curve and data from humidity sensors to execute precise irrigation through a closed-loop Proportion-Integral-Differential (PID) control system. This ensures optimal water management, leading to improved crop quality and yield.

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Zhuravleva, L. A. "Intelligent control System wide-reach Sprinklers circular action." IOP Conference Series: Earth and Environmental Science 1154, no. 1 (March 1, 2023): 012004. http://dx.doi.org/10.1088/1755-1315/1154/1/012004.

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Abstract The relevance of the introduction of intelligent control systems for agricultural machinery in the modern world is increasing every year due to the development of more and more global projects in the field of automation and robotics. The purpose of the research was to develop algorithmic software for an intelligent control system for wide-reach sprinkler machines for autonomous operation in the implementation of environmentally safe, high-quality irrigation. The article considers the possibility of matching the irrigation norm issued by the machine to the level of moisture reserves of the field for circular-acting machines, reducing the environmental load on the soil and preserving fertility. The use of an intelligent control system with the proposed algorithm of operation will improve the quality and environmental safety of irrigation, as well as ensure resource savings, in particular irrigation water savings.

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Mondal, Samrat, Avishek Bhadra, and Shouvik Chakraborty. "A Smart and Intelligent Irrigation System With a Roadmap Ahead." International Journal of Digital Innovation in the Built Environment 10, no. 1 (January 2021): 18–33. http://dx.doi.org/10.4018/ijdibe.2021010102.

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In India, agriculture is an important domain of research for food production. Insufficient, uncertain, and irregular rain causes problems in agriculture, and also, most of the annual rainfall occurs within less than 4 months, for which multiple cropping is not possible. Irrigation is a major influencing factor in agriculture as it solves all these problems. Irrigation helps in stabilising the output and yield levels. The sources of artificial irrigation are wells or canals or some reservoirs, and one also need extra labour to irrigate the fields. Automated and intelligent irrigation can solve many of these problems and reduce the human efforts. Moreover, it also improves the quality of the irrigation by reducing the dependency on the humans. It sends data wirelessly to a central server, which collects the data, stores it, and allow it to be analysed. The results and the collected data can be displayed and data sent to the phone whenever required. In this article, a description of such an intelligent and automated irrigation system is presented.

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Li, Wei. "Unmanned Aerial Vehicle (UAV) in Precision Agriculture to Identify the Crop Water Shortage by Using Multi-Spectral Sensor." Open Access Journal of Agricultural Research 8, no. 2 (2023): 1–4. http://dx.doi.org/10.23880/oajar-16000303.

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Accurate diagnosis of crop water shortage and scientific irrigation decisions are crucial. A new method of multi-spectral imaging remote sensing image extraction of tea canopy temperature is proposed, and an automatic processing system of remote sensing thermal imaging images is established. In this short communication, A UAV (Unmanned Aerial Vehicle) with multi-spectral sensors used to capture the images. The research results show that the system can efficiently mosaic images without image gap, and ensure that the soil background is wholly eliminated. This research gives us new methods to set an intelligent method for precision agriculture, which greatly improves the level of agricultural intelligence.

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G S, Manjunath, and Sudarshan Sudarshan. "Intelligent irrigation system using ML and IoT." International Journal of Research and Innovation in Applied Science VIII, no. V (2023): 01–09. http://dx.doi.org/10.51584/ijrias.2023.8501.

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To realize IoT promise in commercial-scale applications, integrated Internet of Things (IoT) platforms are required. The key challenge is to make the solution flexible enough to fulfill the demands of specific applications. A platform which is IoT-based which is used for smart irrigation with a adaptable design is created so that it allows developers to quickly link IoT and machine learning (ML) components to create application solutions. The design allows for a variety of customized analytical methods for precision irrigation, allowing for the advancement of machine learning techniques. Impacts on many stakeholders may be predicted, including IoT specialists, who would benefit from easier system setup, and farmers, who will benefit from lower costs and safer crop yields. The typical irrigation procedure necessitates a large quantity of use of precious water, which results in waste of water. An intelligent irrigation system is in desperate need to decrease the wastage of water during this tiresome process. Using Machine learning (ML) and the Internet of Things (IoT),it is possible to develop an intelligent system that can accomplish this operation automatically and with minimum human intervention. An system which is enables using IoT and trained using ML is highly recommended and is suggested in this paper for optimum water consumption with minimal farmer interaction. In agriculture, IoT sensors are used to capture exact field and environmental data. The data being collected is transferred and kept in a cloud-based server that uses machine learning to evaluate the data and provide irrigation recommendations.

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Xu, Xiang Hua, Wei Xing Wang, Yue Ming Hu, and Song Bin Zhai. "The Sprinkler Irrigation System Based on Wireless Sensor Networks." Advanced Materials Research 791-793 (September 2013): 1769–73. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1769.

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China is a country with serious shortage of water resources,water resources problem isn't just a matter of resources, but also a significant strategic question related to national economic and social sustainable development. Using efficient intelligent water-saving irrigation technology has become the general trend of irrigation technology all over the world. Is proposed a kind of intelligent irrigation system based on Wireless Sensor Networks (WSN), can be used to realize large area such as landscaping, field crop on a large scale irrigation control,real-time monitoring of large area of soil's temperature and humidity distribution, and keep the block in the most appropriate humidity range to the growth of the crop, which effectively implement water-saving irrigation.

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Journal articles on the topic 'Irrigation intelligente'

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