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Sustainable Precision Agriculture Irrigation System Using Edge Computing and Renewable Energy Integration for Water Conservation and Climate Adaptation Agus Wantoro; Ferly Ardhy; Fahlul Rizki; Ahmad Budi Trisnawan; Yulaikha Mar’atullatifah; Rachmat Setiabudi
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.288

Abstract

The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote sustainable agricultural practices. The study aims to evaluate the performance of the proposed system in terms of water savings, crop yield, energy efficiency, and adaptability to varying climate conditions. Literature Review: Previous studies highlight the importance of smart irrigation systems in reducing water waste and improving crop yield through real time monitoring and automated decision making. However, existing systems often lack the integration of renewable energy and edge computing, which are critical for ensuring sustainability and operational efficiency in rural agricultural settings. The combination of renewable energy with IoT devices offers a promising solution to reduce energy costs and carbon emissions, while edge computing enhances real time data processing, ensuring prompt and accurate irrigation adjustments. Materials and Method: The proposed system integrates solar powered IoT devices, soil moisture sensors, weather data APIs, and edge computing devices to manage irrigation. Machine learning algorithms and evapotranspiration models are used to predict irrigation needs and optimize scheduling based on real time data. The system's performance is evaluated through metrics such as water savings percentage, crop yield improvements, and energy consumption, with a comparative analysis against traditional irrigation methods. Results and Discussion: The results indicate that the system successfully reduces water usage by 30% to 40%, increases crop yield by 25%, and operates with energy autonomy, powered entirely by solar energy. The system's adaptability to varying climate conditions ensures optimal crop growth, even under environmental stresses. The integration of renewable energy and edge computing significantly enhances the sustainability and efficiency of irrigation systems.
Integrating Semantic Computing and Predictive Analytics to Enhance Reliability and Scalability of Global Information Systems Agus Wantoro; Adhie Thyo Priandika; Tiwuk Widiastuti; Yulaikha Mar’atullatifah; Krisna Widi Nugraha; Dwi Utari Iswavigra
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.196

Abstract

Global information systems (GIS) are essential for managing large scale data across industries such as healthcare, finance, and urban planning. As the volume and complexity of data continue to grow, there is an increasing need for systems that can handle these demands while maintaining reliability and scalability. This research explores the integration of semantic computing and predictive analytics as a solution to improve the performance of GIS. Semantic computing, through the use of ontologies and standardized data models, enhances data interoperability, allowing systems to interpret and exchange data meaningfully across diverse platforms. On the other hand, predictive analytics uses statistical methods and machine learning models to forecast system behavior and optimize resource allocation, ensuring systems remain adaptive under varying loads. By integrating these two methodologies, this study demonstrates how they can address key challenges in global information systems, such as fault tolerance, system adaptability, and real time decision making. The results show significant improvements in system reliability and scalability, as well as better performance under high data volumes and diverse user interactions. The integrated approach was tested in several use cases, including urban planning, healthcare, and supply chain management, with results indicating that systems utilizing both semantic computing and predictive analytics are more resilient, accurate, and efficient. This paper discusses the practical implications of this integration for global scale applications and suggests future research directions, including the incorporation of emerging technologies like blockchain and artificial intelligence to further enhance the capabilities of GIS.