Sustainable Farming Through AI-Enabled Precision Agriculture

Abstract

Modern agricultural techniques have seen revolutionary improvements because of Precision Agriculture's quick development, which is driven by the incorporation of artificial intelligence (AI). In-depth information on the symbiotic relationship between AI and Precision Agriculture is presented in this survey study, which emphasizes significant features ranging from fundamental AI ideas to real-world applications. The work discovers a foundational grasp of AI's historical evolution and wide areas of application by beginning with an intelligent introduction to AI. The review then digs into the world of machine learning algorithms, analyzing decision trees, support vector machines, and random forests, and illuminating their vital roles in crop monitoring, disease diagnosis, and yield forecasting. The focus of the investigation moves to deep learning methods, particularly convolutional neural networks and recurrent neural networks, which are used for tasks including yield computation, plant phenotyping, and image analysis. An in-depth analysis of the complex AI model engineering process reveals the iterative path from issue conceptualization to model optimization. The capabilities of TensorFlow, PyTorch, and specialized AutoML tools targeted to the subtleties of agricultural situations are clarified, drawing attention to AI model generation platforms. A study on converting AI models into microservices powered by application programming interfaces (APIs) demonstrates their potential for seamless integration with agricultural systems, raising real-world application. When navigating the deployment phase, the survey takes into account the subtleties of edge computing, cloud deployment, and hybrid architectures to traverse the various techniques for installing AI models in Precision Agriculture. Through insights into data integration and ingestion approaches from various sources, such as remote sensing and IoT devices, the synergy between AI and data is addressed. In addition, the study includes data analytics techniques for extracting forecasts and actionable insights from complicated agricultural data. It highlights the critical role of knowledge visualization tools in bridging the gap between data-driven insights and well-informed decision-making. The study demystifies the fundamental principles of precision and smart agriculture and shows how they have the potential to revolutionize sustainable practices, yield optimization, and resource utilization. The survey article emphasizes the revolutionary potential of AI in Precision Agriculture as a final point, uniting theoretical underpinnings with practical applications. This article provides readers with a comprehensive overview of the dynamic environment where AI and agriculture collide, paving the road for a more sustainable and technologically advanced future by synthesizing a wide range of issues

Topics & Concepts