Impact of Cloud Computing on Agricultural Advancement using Data Mining Algorithms

Saravanan, S. K.; Nisha, F.; Rohit, V. Robin; Lenin, J.; Selvam, P D; Rajmohan, M.

doi:10.1109/icacrs58579.2023.10404669

Cited by 1 publication

(3 citation statements)

References 16 publications

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“…The complexity of data management and analysis in precision agriculture, compounded by the vast amount of data generated by IoT devices and sensors, presents another significant challenge. Saravanan et al (2023) highlight the impact of cloud computing on agricultural advancement through the use of data mining algorithms. By incorporating Internet of Things (IoT) algorithms, Ensemble Learning, and Explainable AI (XAI) into the Reinforcement Learning framework, the study presents a dynamic paradigm for precision agriculture that increases output while decreasing resource consumption.…”

Section: Challenges and Solutions In Implementing Self-learning Algor...mentioning

confidence: 99%

“…Adaptive AI in agriculture represents a paradigm shift from traditional farming practices, which were largely reactive and based on historical data, to a more proactive, data-driven approach. Saravanan et al (2023) highlight the role of cloud computing in agricultural advancement, emphasizing the integration of Internet of Things (IoT) algorithms, Ensemble Learning, and Explainable AI (XAI) within a Reinforcement Learning framework. This integration facilitates a dynamic, real-time adaptation to environmental changes, significantly enhancing output while reducing resource consumption (Saravanan et al, 2023).…”

Section: Introduction 1the Emergence Of Adaptive Ai In Modern Agricul...mentioning

confidence: 99%

“…Saravanan et al (2023) highlight the role of cloud computing in agricultural advancement, emphasizing the integration of Internet of Things (IoT) algorithms, Ensemble Learning, and Explainable AI (XAI) within a Reinforcement Learning framework. This integration facilitates a dynamic, real-time adaptation to environmental changes, significantly enhancing output while reducing resource consumption (Saravanan et al, 2023). The ability of agricultural machinery to collect and exchange data with cloud servers not only improves precision but also flexibility in farm operations, marking a significant leap towards the realization of smart farming.…”

Section: Introduction 1the Emergence Of Adaptive Ai In Modern Agricul...mentioning

confidence: 99%

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Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data

Olabimpe Banke Akintuyi

2024

Open Access Res. J. Multidiscip. Stud.

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This study investigates the transformative impact of adaptive Artificial Intelligence (AI) on precision agriculture, focusing on optimizing farm operations through real-time data analysis. The primary objective was to assess how adaptive AI technologies enhance the efficiency, productivity, and sustainability of agricultural practices. Employing a systematic literature review and content analysis, the methodology involved scrutinizing peer-reviewed articles and grey literature from key databases, applying stringent inclusion and exclusion criteria to ensure relevance and quality. Key findings reveal that adaptive AI significantly improves farm operations by enabling precise monitoring and management of crops, soil, and environmental conditions. The integration of IoT devices and machine learning algorithms facilitates real-time data analysis, leading to optimized resource use, reduced environmental impact, and increased crop yields. Economic benefits include cost savings through efficient resource management, while environmental advantages encompass minimized chemical use and enhanced sustainability. Challenges identified include high implementation costs, technical complexity, and data privacy concerns. However, solutions such as policy support, technological advancements, and stakeholder collaboration are proposed to overcome these barriers. Lastly, adaptive AI holds the potential to revolutionize precision agriculture by making it more efficient, sustainable, and productive. Future research should focus on developing accessible, robust AI solutions and fostering an environment conducive to technological adoption. The study underscores the need for continued innovation and policy support to fully realize the benefits of AI in agriculture.

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Section: Challenges and Solutions In Implementing Self-learning Algor...mentioning

confidence: 99%

Section: Introduction 1the Emergence Of Adaptive Ai In Modern Agricul...mentioning

confidence: 99%

Section: Introduction 1the Emergence Of Adaptive Ai In Modern Agricul...mentioning

confidence: 99%

See 1 more Smart Citation

Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data

Olabimpe Banke Akintuyi

2024

Open Access Res. J. Multidiscip. Stud.

View full text Add to dashboard Cite

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Impact of Cloud Computing on Agricultural Advancement using Data Mining Algorithms

Cited by 1 publication

References 16 publications

Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data

Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data

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