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CropSynergy: Harnessing IoT solutions for smart and efficient crop management
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DOI：10.1016/j.cropd.2025.100127.png)
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Abstract
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Agriculture has been a cornerstone of human civilization and continues evolving to meet the growing global population's demands. In an era of rapid technological advancement, the integration of smart agriculture, also known as smart farming or precision farming, has become essential for sustainable agricultural practices. This approach uses the Internet of Things (IoT) and Artificial Intelligence (AI) to improve data collection and analysis, facilitating real-time monitoring of crop health, soil conditions, irrigation, and comprehensive farm management. By incorporating IoT, farmers can make informed decisions, optimize resource use, and improve crop yields. This study explores the scope of integrating IoT into crop management to optimize traditional crop-related issues and to improve the quality, production, and marketing process. Our in-depth analysis illustrates the IoT architecture, the IoT in smart farming, and how IoT sensors and devices collect real-time data on soil moisture, temperature, humidity, and other environmental factors, which are then processed using cloud computing platforms and stored accordingly. After conducting an exhaustive search with sorted keywords aligned with our research interests, we have identified more than a thousand research articles. A further filtering approach is applied based on inclusion and exclusion criteria to select a significant number of well-suited articles, which are thoroughly analyzed in the development of this systematic review paper. The growing number of literature in this field has created a vast area for exploration. In response, we have thoroughly studied all the aspects of IoT and have proposed a novel taxonomy for IoT to systematically classify and analyze key aspects like Sensors, Actuators, Development Board, and Communication. We examine the application of IoT in crop management, emphasizing its benefits, challenges, and future potential. In addition, we analyze key IoT components, such as sensors, data analytics, and decision-making tools, and explore their role in enhancing agricultural sustainability, economic viability, and food security. Finally, we concluded our study by addressing the challenges and outlining future directions for further advancement.
Keywords:
Machine learning
Smart agriculture
Precision farming
Cloud computing
Irrigation system
Wireless sensor networks
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