A Hybrid Method for Smart Irrigation System

The rapid advancement of IoT (Internet of Things) technologies and sophisticated machine learning models is driving innovation in irrigation systems, laying the foundation for more effective and eco-friendly smart agricultural procedures. This systematic literature review strives to uncover the advancements and challenges in the advancement and implementation of IoT-based smart irrigation systems integrated with advanced machine learning techniques. By analyzing 43 relevant studies published between 2017 and 2024, the research focuses on the ability of these technologies have evolved to meet the challenges of modern agriculture irrigation system. Predictive analytics, anomaly detection, and adaptive control—that enhance irrigation precision and decision-making processes. Employing the PRISMA methodology, this review uncovers the strengths and limitations of current systems, highlighting significant achievements in real-time data utilization and system responsiveness. However, it also brings attention to unresolved issues, including the complexities of data integration, network reliability, and the scalability of IoT-based frameworks. Additionally, the study identifies crucial gaps in standardization and the need for flexible solutions that can adapt to diverse environmental conditions. By offering a comprehensive analysis, this review provides key insights for advancing smart irrigation technologies, emphasizing the importance of continued research in overcoming the existing barriers to wider adoption and effectiveness in various agricultural settings.

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Cloud-based Internet of Things Approach for Smart Irrigation System

Ganesan,

Hameed,

Maruthakutti

2024

IJITEE

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The agriculture sector is facing numerous challenges such as water scarcity, climate change, and low productivity due to outdated farming practices. In most of the countries, 75% of their GDP depends on the agricultural products. Hence at most care has to be taken to introduce modern technology in the agriculture sector to enhance its productivity and efficiency. This paper presents an IOT Based Smart Agriculture Monitoring System aimed to increase the agricultural productivity through automating and optimizing crop management. The first objective of the proposed system is to supply the water to the crops based on soil moisture and the surrounding temperature. It can be achieved through soil moisture sensor, DHT11 sensor, light sensitive module and Node MCU. All sensors are interfaced with Node MCU. The data collected through sensor will be processed by the processor. Whenever the soil moisture is less and temperature is more, the pump will be automatically switched on for irrigation. Apart from this the collected data from sensor is also transmitted to the cloud through Thingspeak. The same information can be accessed by the farmers to monitor the crops remotely and improve the productivity. By providing farmers with real time data on their crops, the system can help them make informed decisions regarding water and fertilizer usage, pest control, and harvesting times. This, in turn, can lead to increased crop yields, reduced costs, and improved profitability. With the increasing demand for food production and the need to address the challenges of climate change and food security, this project serves as a promising solution for sustainable agriculture.

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A Hybrid Method for Smart Irrigation System

Cited by 4 publications

References 4 publications

Experimental performance of smart IoT-enabled drip irrigation system using and controlled through web-based applications

Experimental performance of smart IoT-enabled drip irrigation system using and controlled through web-based applications

A Smart Irrigation System Using the IoT and Advanced Machine Learning Model

Cloud-based Internet of Things Approach for Smart Irrigation System

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