Transforming Agriculture through AI: A Technical Perspective on Next-Generation Farming

This comprehensive technical article explores the transformative impact of Artificial Intelligence in modern agriculture, examining the integration of advanced technologies across multiple domains including aerial surveillance, IoT infrastructure, soil analytics, and weather forecasting. The article investigates how AI-driven systems have revolutionized traditional farming practices through sophisticated computer vision, machine learning algorithms, and sensor networks. The article demonstrates significant advancements in crop monitoring, resource optimization, and risk management through the implementation of multi-layered architectural frameworks. The article encompasses various technological components including drone-based surveillance, soil monitoring systems, weather prediction models, and integrated decision support systems, highlighting their collective contribution to enhancing agricultural productivity and sustainability.

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Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2025-03-20
DOI
https://doi.org/10.32628/cseit25112696
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Transforming Agriculture through AI: A Technical Perspective on Next-Generation Farming

Sandeep Reddy Pakeer
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Smart Agriculture and AI
article

Transforming Agriculture through AI: A Technical Perspective on Next-Generation Farming

Sandeep Reddy Pakeer
article en

Abstract

This comprehensive technical article explores the transformative impact of Artificial Intelligence in modern agriculture, examining the integration of advanced technologies across multiple domains including aerial surveillance, IoT infrastructure, soil analytics, and weather forecasting. The article investigates how AI-driven systems have revolutionized traditional farming practices through sophisticated computer vision, machine learning algorithms, and sensor networks. The article demonstrates significant advancements in crop monitoring, resource optimization, and risk management through the implementation of multi-layered architectural frameworks. The article encompasses various technological components including drone-based surveillance, soil monitoring systems, weather prediction models, and integrated decision support systems, highlighting their collective contribution to enhancing agricultural productivity and sustainability.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 11(2)
Georgia Institute of Technology (US)
Openalex Percentile: Top 96%
Smart Agriculture and AI
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