This study explores the implementation of intelligent systems in agriculture as a solution to longstanding challenges such as inefficient resource use, disease management, and low productivity. By integrating technologies like Artificial Intelligence (AI), the Internet of Things (IoT), computer vision, and robotics, intelligent systems enable precision farming that optimizes water usage, enhances crop monitoring, automates labor-intensive tasks, and improves overall decision-making. Real-world applications such as CropX and NetBeat for smart irrigation, Plantix and Nuru for disease detection, and John Deere’s autonomous tractors for automated fieldwork demonstrate the tangible benefits of these innovations. Additionally, tools like Moocall and Ida offer real-time livestock health monitoring, while platforms such as AgriPredict and aWhere provide data-driven decision support to farmers globally. A sample block diagram of a smart irrigation system, supported by a simplified calculation, illustrates the practical operation and measurable benefits of such systems. The study emphasizes the potential of intelligent agriculture not only to boost productivity and sustainability but also to make advanced tools more accessible to small and medium-scale farmers. Future advancements should aim to enhance integration, affordability, and ease of use, ultimately supporting the transition to more resilient and efficient agricultural practices in the face of growing global food demands.
Ican Anwar (Sat,) studied this question.