Optimizing Agricultural Decision-Making with Artificial Intelligence: A COPRAS-Based Evaluation

[1]. Artificial intelligence (AI) in agriculture has the potential to revolutionize farming practices, improving productivity, sustainability, and decision-making. This study explores the application of AI in agriculture, focusing on how AI-driven tools and systems can optimize various farming processes, such as crop management, pest control, and yield prediction. The research evaluates the role of AI in addressing challenges faced by farmers and its impact on agricultural efficiency. The findings highlight the significant benefits of AI technologies in modernizing agriculture and creating sustainable farming solutions. Research Significance: The significance of this research lies in its ability to showcase the transformative potential of artificial intelligence in agriculture. AI applications can enhance precision farming, reduce resource wastage, and improve crop yields, all of which contribute to better sustainability and profitability for farmers. The study explores how AI technologies can address key challenges in agriculture, such as labor shortages, unpredictable weather patterns, and the need for optimized resource use. The findings offer valuable insights into how AI can drive agricultural innovation and help ensure food security in an increasingly volatile global environment. Methodology: COPRAS The COPRAS (Complex Proportional Assessment) methodology is utilized to assess and compare various AI-driven agricultural solutions and technologies. By evaluating multiple alternatives based on their effectiveness in improving agricultural practices, COPRAS provides a structured approach to selecting the most suitable AI solutions. The methodology takes into account factors such as cost-effectiveness, efficiency, and scalability, helping to identify the most impactful AI technologies for different agricultural needs. Alternative: AI-based crop monitoring systems, AI-driven irrigation technologies, AIpowered pest detection tools, AI-enhanced yield prediction models, AI-assisted supply chain management. Evaluation Parameters: Product price (C1), Company rating (C2), Delivery time (C3), Transportation costs (C4). Result: The results of the COPRAS analysis demonstrate that AI-driven technologies have the potential to significantly improve agricultural outcomes. AIpowered systems that offer efficient crop monitoring, precise irrigation, and early pest detection provide high value to farmers. Technologies with lower costs and faster deployment times, such as AI-based irrigation systems, tend to rank higher in the evaluation, making them more attractive to farmers seeking affordable solutions. However, the effectiveness and scalability of the solution are also crucial factors influencing the overall ranking