Published Paper Details:

The integration of Artificial Intelligence (AI) in agriculture, often termed "smart farming," is transforming traditional agricultural practices into data-driven, optimized systems that increase productivity and sustainability. With the global population projected to surpass 9 billion by 2050, conventional farming methods alone may not suffice to meet the escalating food demands. Smart farming, powered by AI technologies, addresses critical challenges in agriculture by enhancing crop monitoring, enabling precision irrigation, improving soil analysis, and optimizing pest and disease control. This research paper explores the deployment of AI-driven tools, such as machine learning algorithms, Internet of Things (IoT) devices, and drones, to revolutionize various agricultural tasks. Machine learning algorithms analyze environmental and crop-related data to predict ideal planting and harvesting periods, assess soil health, and optimize resource distribution. IoT sensors provide real-time data on factors such as soil moisture, temperature, and humidity, allowing for automated irrigation systems that significantly reduce water waste. Additionally, computer vision and deep learning enable precise monitoring and diagnosis of plant diseases, supporting timely interventions and reducing the need for chemical treatments. The adoption of AI in agriculture not only enhances efficiency and crop yields but also minimizes environmental impact by reducing resource consumption and improving land utilization. This paper further highlights the practical applications of AI in smart farming and discusses the challenges and future opportunities for AI integration in agriculture. Ultimately, the findings underscore AI's potential to make farming more resilient, sustainable, and capable of supporting global food security.