Sensor Data Acquisition and Analysis in Indoor Farming Using Azure IoT Hub

Indoor farming is one of the agricultural innovations, providing essential support in controlled environments that reduced many traditional farming challenges.However, they face unique issues in managing their crops effectively, from optimizing limited space to controlling environmental conditions within their facilities.While traditional indoor farming methods provide a foundation, they often lack the precision and real-time insights needed for optimal plant health and yield.This paper explores the application of Azure IoT Hub in indoor farming, focusing on how this technology facilitates enhanced data analysis to improve agricultural efficiency and productivity.In the realm of precision farming, Azure IoT Hub plays an important role by enabling seamless integration and management of IoT-enabled sensors.By integrating sensors into the green house, users can access real-time data on crucial parameters such as temperature, moisture levels, and potential nutrient issues.This data is then processed through Azure Stream Analytics, enabling users to make data-driven decisions with precision.These sensors are critical for monitoring variables such as water level, temperature, nutrient levels, and pH, allowing for precise control over irrigation and fertilization schedules.By implementing Azure IoT Hub, the paper demonstrates how real-time data acquisition and analysis can be optimized, ensuring that optimal growth conditions are consistently maintained.This system not only supports the immediate detection and response to environmental changes but also provides a framework for predictive analytics, which is crucial for handling issues that could adversely affect plant health and crop yield.

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Publication Details

Journal
Juniper Online Journal Material Science
Published
2025-04-29
DOI
https://doi.org/10.19080/jojms.2025.09.555768
Primary Topic
Smart Agriculture and AI
Type
article
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article

Sensor Data Acquisition and Analysis in Indoor Farming Using Azure IoT Hub

Nezha Kharraz, István Szabó
Juniper Online Journal Material Science
Smart Agriculture and AI
article

Sensor Data Acquisition and Analysis in Indoor Farming Using Azure IoT Hub

Nezha Kharraz, István Szabó
article en

Abstract

Indoor farming is one of the agricultural innovations, providing essential support in controlled environments that reduced many traditional farming challenges.However, they face unique issues in managing their crops effectively, from optimizing limited space to controlling environmental conditions within their facilities.While traditional indoor farming methods provide a foundation, they often lack the precision and real-time insights needed for optimal plant health and yield.This paper explores the application of Azure IoT Hub in indoor farming, focusing on how this technology facilitates enhanced data analysis to improve agricultural efficiency and productivity.In the realm of precision farming, Azure IoT Hub plays an important role by enabling seamless integration and management of IoT-enabled sensors.By integrating sensors into the green house, users can access real-time data on crucial parameters such as temperature, moisture levels, and potential nutrient issues.This data is then processed through Azure Stream Analytics, enabling users to make data-driven decisions with precision.These sensors are critical for monitoring variables such as water level, temperature, nutrient levels, and pH, allowing for precise control over irrigation and fertilization schedules.By implementing Azure IoT Hub, the paper demonstrates how real-time data acquisition and analysis can be optimized, ensuring that optimal growth conditions are consistently maintained.This system not only supports the immediate detection and response to environmental changes but also provides a framework for predictive analytics, which is crucial for handling issues that could adversely affect plant health and crop yield.

Juniper Online Journal Material ScienceVol. 9(4)
Hungarian School Sport Federation (HU), Hungarian Academy of Sciences (HU)
Openalex Percentile: Top 93%
Smart Agriculture and AI
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