Design of Smart Agriculture Systems using Artificial Intelligence and Big Data Analytics

Katyayan, Amatya; Mashelkar, Sheena; Gama, Alroy; Morajkar, Sweta C.

doi:10.1109/icac3n53548.2021.9725672

Cited by 4 publications

(2 citation statements)

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“…The advent of precision agriculture, employing satellite and drone technology, sensor-based monitoring systems, and AI applications, revolutionized farming by enabling precise resource management and data-driven decision-making (Nemade et al, 2023). Katyayan et al (2021) discuss the design of smart agriculture systems using AI and big data analytics, emphasizing the need to harness data in new ways to provide better insights and advisory. The paper outlines the use of on-ground sensors to gather personalized real-time data remotely, managed using data analytics to offer precise, relevant, and spontaneous solutions.…”

Section: From Traditional Farming Practices To Real-time Data-driven ...mentioning

confidence: 99%

“…The integration of Natural Language Processing (NLP) and computer vision allows for real-time analysis of crop images through Machine Learning (ML) algorithms, providing farming-related remedies around the clock. This approach signifies a departure from traditional methodologies, leveraging technology to optimize agricultural practices and ensure food security for a growing global population (Katyayan et al, 2021). Gomaa (2023) explores modern trends in the development of smart agriculture projects, highlighting the pivotal role of IoT and sensor technology.…”

Section: From Traditional Farming Practices To Real-time Data-driven ...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: From Traditional Farming Practices To Real-time Data-driven ...mentioning

confidence: 99%

Section: From Traditional Farming Practices To Real-time Data-driven ...mentioning

confidence: 99%

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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Smart Agriculture Solutions: Harnessing AI and IoT for Crop Management

Alazzai,

Obaid,

Abood

et al. 2024

E3S Web Conf.

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This review article, “Smart Agriculture Solutions: Harnessing AI and IoT for Crop Management,” explores the transformative impact of digital technologies in agriculture. It focuses on the integration of the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) in enhancing sustainable farming practices. IoT technologies enable real-time monitoring of agricultural conditions using sensors and drones, while Cloud Computing provides robust data processing capabilities. AI leverages this data for predictive analytics in crop yields, disease detection, and automation of farming processes, optimizing resource use and adapting to climate change. The paper also examines the synergy of renewable energy sources with AI to power smart farming systems, proposing innovative solutions for energy-efficient, AI-driven agriculture. It highlights AI’s extensive role in improving crop management, from seed selection to soil analysis and crop health monitoring, thereby increasing yield quality and fostering sustainability. Furthermore, the article discusses the significance of space technologies in smart agriculture, showcasing advancements like drones and big data analytics. These innovations are crucial in developing economies, addressing global challenges like food security and contributing to economic growth. This review underlines the necessity of AI and IoT in revolutionizing agricultural methodologies, ensuring efficiency and sustainability in the face of a rapidly growing global population.

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Using Computer Vision and Machine Learning Based Methods for Plant Monitoring in Agriculture: A Systematic Literature Review

Kempelis

Romānovs

Patļins

2022

2022 63rd International Scientific Conference on Information Technology and Management Science of Riga Technical University (IT

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Design of Smart Agriculture Systems using Artificial Intelligence and Big Data Analytics

Cited by 4 publications

References 3 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

Smart Agriculture Solutions: Harnessing AI and IoT for Crop Management

Using Computer Vision and Machine Learning Based Methods for Plant Monitoring in Agriculture: A Systematic Literature Review

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