Artificial Intelligence based Robotic System with Enhanced Information Technology

Venice, J. Arockia; Thoti, Kiran Kumar; Henrietta, H. Mary; Elangovan, Muniyandy; Anusha, D.; Zhakupova, Aliya

doi:10.1109/i-smac55078.2022.9987367

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…AI spans several sub-domains: machine learning uncovers data patterns without direct programming [47], computer vision gives machines sight [48], and natural language processing enables them to understand and produce human language [49]. Expert systems represent human knowledge in structured domains [50], and robotics combines these AI capabilities for environmental interaction [51][52][53]. AI can be general, with human-like cognitive abilities, or narrow, focused on specific tasks, where most advancements occur, revolutionizing industries with applications like smart assistants and fraud detection [54,55].…”

Section: Definition and Basics Of Aimentioning

confidence: 99%

Artificial Intelligence: A Promising Tool for Application in Phytopathology

González-Rodríguez,

Izquierdo-Bueno,

Cantoral

et al. 2024

Horticulturae

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Artificial intelligence (AI) is revolutionizing approaches in plant disease management and phytopathological research. This review analyzes current applications and future directions of AI in addressing evolving agricultural challenges. Plant diseases annually cause 10–16% yield losses in major crops, prompting urgent innovations. Artificial intelligence (AI) shows an aptitude for automated disease detection and diagnosis utilizing image recognition techniques, with reported accuracies exceeding 95% and surpassing human visual assessment. Forecasting models integrating weather, soil, and crop data enable preemptive interventions by predicting spatial-temporal outbreak risks weeks in advance at 81–95% precision, minimizing pesticide usage. Precision agriculture powered by AI optimizes data-driven, tailored crop protection strategies boosting resilience. Real-time monitoring leveraging AI discerns pre-symptomatic anomalies from plant and environmental data for early alerts. These applications highlight AI’s proficiency in illuminating opaque disease patterns within increasingly complex agricultural data. Machine learning techniques overcome human cognitive constraints by discovering multivariate correlations unnoticed before. AI is poised to transform in-field decision-making around disease prevention and precision management. Overall, AI constitutes a strategic innovation pathway to strengthen ecological plant health management amidst climate change, globalization, and agricultural intensification pressures. With prudent and ethical implementation, AI-enabled tools promise to enable next-generation phytopathology, enhancing crop resilience worldwide.

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Section: Definition and Basics Of Aimentioning

confidence: 99%

Artificial Intelligence: A Promising Tool for Application in Phytopathology

González-Rodríguez,

Izquierdo-Bueno,

Cantoral

et al. 2024

Horticulturae

View full text Add to dashboard Cite

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“…One semi-autonomous option is a tractor-pulled tool that uses computer vision for precise weeding. The recent advancement in camera vision and AI, particularly Deep Learning approaches, shows promise for weed identification in cash crops [10]. Harvesting: Autonomous fruit picking has received a lot of focus in greenhouse horticulture.…”

Section: Evolution Of Technology In Agriculturementioning

confidence: 99%

Technological Applications in Smart Farming: A Bibliometric Analysis

Jonitha Anand,

Nooraini Yusoff,

Hadhrami Ab Ghani

et al. 2023

ASET

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Precision agriculture, often known as smart farming, employs cutting-edge technology to change farming processes. This paper covers smart farming's history, important advancements, and possible impacts on agriculture. The essay begins with the history of "smart farming," or precision agriculture, and how GPS technology improved fertilization, pest control, and irrigation. Since then, automation and sensor technology have enabled continuous crop vitality, moisture, and environmental monitoring, enabling empirical decision-making. Networking technologies, especially the Internet of Things, have transformed smart farming. Farmers may gather, analyze, and apply real-time data using connected drones, satellite images, and farm management software. Big data analytics and AI allow farmers to leverage enormous datasets for crop health monitoring, production forecasts, and resource management. Smart farming improves accuracy and sustainability. By using resources efficiently, farmers may reduce their environmental impact. Due to remote monitoring and control, farmers can adapt to changing conditions and manage their operations from anywhere. Smart farming has numerous benefits, but various barriers limit its broad implementation. Due to expensive startup costs, specialized training, and limited Internet connection, some farmers are unwilling to use these technologies. Governments, NGOs, and industry actors must collaborate to provide financing, training, and improved connections to address these difficulties. This review and study of smart farming's growth highlights its revolutionary potential in agriculture. Smart farming may enhance output, optimize input returns, and ensure long-term viability, making it an exciting new route for agriculture.

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Artificial Intelligence based Robotic System with Enhanced Information Technology

Cited by 2 publications

References 36 publications

Artificial Intelligence: A Promising Tool for Application in Phytopathology

Artificial Intelligence: A Promising Tool for Application in Phytopathology

Technological Applications in Smart Farming: A Bibliometric Analysis

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