Title: Artificial Intelligence: Theory, Tools and Real-World Applications
Editors: Prof. Dr. A. Shameem and Dr. J. Rengamani
ISBN: 978-81-69857-67-3
Chapter: 10
DOI: https://doi.org/10.59646/819/11
Author: Mr. Joel Smith J
Abstract
Artificial Intelligence (AI) is transforming modern agriculture by enabling data-driven decision-making, improving productivity, and promoting sustainable farming practices. Smart agriculture integrates AI technologies such as machine learning, computer vision, the Internet of Things (IoT), and remote sensing to enhance precision farming and crop monitoring. These technologies allow farmers to collect and analyze real-time data related to soil conditions, weather patterns, crop health, irrigation requirements, and pest infestations. AI-powered systems can accurately predict crop yields, identify diseases at early stages, optimize fertilizer usage, and automate farming operations, thereby reducing costs and increasing efficiency. Precision farming techniques supported by AI help minimize resource wastage while maximizing agricultural output, contributing to food security in the face of growing global population demands. Furthermore, drone-based imaging and satellite monitoring provide continuous surveillance of large agricultural fields, enabling timely interventions and improved crop management. Despite challenges such as high implementation costs, data privacy concerns, and limited technological infrastructure in rural regions, AI-driven smart agriculture offers significant opportunities for sustainable agricultural development. This study examines the practical applications of Artificial Intelligence in precision farming and crop monitoring, highlighting its benefits, technological advancements, challenges, and future prospects. The findings indicate that AI has the potential to revolutionize agricultural practices by improving productivity, profitability, and environmental sustainability.
Keywords: Artificial Intelligence, Smart Agriculture, Precision Farming, Crop Monitoring, Machine Learning, Internet of Things (IoT), Computer Vision, Remote Sensing, Agricultural Automation, Crop Yield Prediction, Sustainable Farming, Drone Technology.