Intelligent Control of Irrigation Systems Using Fuzzy Logic Controller

Singh, Arunesh Kumar; Tariq, Tabish; Ahmer, Mohammad F.; Bokoro, Pitshou N.; Shongwe, Thokozani

doi:10.3390/en15197199

Cited by 12 publications

(8 citation statements)

References 26 publications

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“…In [33], authors present a fuzzy logic algorithm for smart irrigation with comparison of two methods (Mamdani and Sugeno) for open and closed fuzzy logic control system in MATLAB, where more number of input variables are used to extract the data which create complexity. Similarly, the authors in [34] implement a fuzzy logic control system for irrigation, controlling the pump speed, an IoT network is deployed, and the authors claim an improvement in irrigation with a reduced workforce. Authors in [35] present an irrigation system on chili plant, where a fuzzy logic control system is connected with an IoT system for controlling and monitoring management.…”

Section: Related Work and Research Methodsmentioning

confidence: 99%

Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology

Bin,

Shahzad,

Khan

et al. 2023

Sustainability

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Sustainable agriculture is a pivotal driver of a nation’s economic growth, especially considering the challenge of providing food for the world’s expanding population. Agriculture remains a cornerstone of many nations’ economies, so the need for intelligent, sustainable farming practices has never been greater. Agricultural industries worldwide require sophisticated systems that empower farmers to manage their crops efficiently, reduce water wastage, and optimize yield quality. Yearly, substantial crop losses occur due to unpredictable environmental changes, with improper irrigation practices being a leading cause. In this paper, we introduce an innovative irrigation time control system for smart farming. This system leverages fuzzy logic to regulate the timing of irrigation in cotton crop fields, effectively curbing water wastage while ensuring that crops receive neither too little nor too much water. Additionally, our system addresses a common agricultural challenge: whitefly infestations. Users can adjust climatic parameters, such as temperature and humidity, through our system, which minimizes both whitefly populations and water consumption. We have developed a portable measurement technology that includes air humidity sensors, temperature sensors, and rain sensors. These sensors interface with an Arduino platform, allowing real-time climate data collection. This collected climate data is then sent to the fuzzy logic control system, which dynamically adjusts irrigation timing in response to changing environmental conditions. Our system incorporates an algorithm that generates highly effective (IF-THEN) fuzzy logic rules, significantly improving irrigation efficiency by reducing overall irrigation duration. By automating the irrigation process and precisely delivering the right amount of water, our system eliminates the need for human intervention, rendering the agricultural system more dependable in achieving successful crop yields. Water supply commences when the environmental conditions reach specific thresholds and halts when the requisite climate conditions are met, maintaining an optimal environment for crop growth.

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Section: Related Work and Research Methodsmentioning

confidence: 99%

Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology

Bin,

Shahzad,

Khan

et al. 2023

Sustainability

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“…The irrigation controller proposed in this study is an intelligent water-level controller for irrigated rice fields based on fuzzy logic (Zadeh & Aliev, 2018). This method is currently used to add computational intelligence to irrigation systems, as demonstrated by Ibrahim et al (2018), Khatri (2018), Mendes et al (2019), Krishnan et al (2020), Azry et al (2022), andSingh et al (2022). The main difference between the proposed controller and the controllers from the aforementioned studies is the response to weather forecast.…”

Section: Methodsmentioning

confidence: 99%

Rice-irrigation automation using a fuzzy controller and weather forecast

Uberti

Abaide

Pfitscher

et al. 2023

Rev. bras. eng. agríc. ambient.

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This paper presents a new irrigation controller based on fuzzy logic that uses weather forecast data and crop characteristics to evaluate the real-time need for irrigation of rice crops and to increase the efficiency of irrigation systems. Tests were performed with real data obtained from three different crop fields in Rio Grande do Sul State, Brazil, and on four meteorologically different days of the 2021/2022 harvest to demonstrate the ability to reduce power consumption for irrigation; the power consumption on days of heavy precipitation was above 80% under all simulated conditions. Depending on the size of the crop and the tested meteorological conditions, the minimum reductions in energy consumption were between 33-66% on dry days with no precipitation forecast. More than 15% reduction in the flow of the water catchment was also observed, even in the most adverse farming scenarios. This study reveals the necessity for technological advances in rice-crop irrigation systems to increase the efficiency of flood irrigation in large areas for reducing electricity consumption, increasing the profitability of rural producers, and ensuring the preservation and availability of water resources.

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“…By designing the network structure of shunt processing and compressing the traditional ENet network, the accuracy of the beet field boundary's location and row-to-row segmentation was improved. Faced with overwatering in irrigation systems, Singh et al [178] designed a MAMDANI fuzzy inference method, which was applied to dryland irrigation robots to optimize the acquisition and processing of crop row information, helping to better control water flow and automate dryland crop irrigation.…”

Section: Row Detection For Irrigationmentioning

confidence: 99%

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

et al. 2023

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Crop row detection is one of the foundational and pivotal technologies of agricultural robots and autonomous vehicles for navigation, guidance, path planning, and automated farming in row crop fields. However, due to a complex and dynamic agricultural environment, crop row detection remains a challenging task. The surrounding background, such as weeds, trees, and stones, can interfere with crop appearance and increase the difficulty of detection. The detection accuracy of crop rows is also impacted by different growth stages, environmental conditions, curves, and occlusion. Therefore, appropriate sensors and multiple adaptable models are required to achieve high-precision crop row detection. This paper presents a comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation. Particular attention has been paid to the sensors and systems used for crop row detection to improve their perception and detection capabilities. The advantages and disadvantages of current mainstream crop row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized. Additionally, the applications for different crop row detection tasks, including irrigation, harvesting, weeding, and spraying, in various agricultural scenarios, such as dryland, the paddy field, orchard, and greenhouse, are reported.

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Intelligent Control of Irrigation Systems Using Fuzzy Logic Controller

Cited by 12 publications

References 26 publications

Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology

Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology

Rice-irrigation automation using a fuzzy controller and weather forecast

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

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