AI vs. traditional farming: A comparative economic analysis of sugarcane production in Pune District of Maharashtra

The present study targeted to compare the cost of cultivation, returns, and profitability between AI (Artificial Intelligence) and Non-AI sugarcane farmers in the Pune district of Maharashtra. With the emergence of AI technologies in agriculture such as remote sensing, automated irrigation, disease detection, and yield forecasting farmers have started exploring technology-driven methods to enhance efficiency. This study focused on analysing the differences in cost components including Cost A, Cost B and Cost C between AI and Non-AI adopters. Primary data was collected through structured interviews and surveys. The findings revealed that AI farmers incurred lower variable costs, particularly in labor, fertilizer, and plant protection due to precise input use. Although AI adoption required some initial investment, it significantly reduced operational inefficiencies. On the other hand, Non-AI farmers continued to rely on traditional practices, resulting in relatively higher costs and stagnant productivity. The study also observed that the profitability of AI farmers was comparatively higher due to better input management and increased returns. Therefore, the integration of AI in sugarcane cultivation shows promising results in improving cost-effectiveness and farm income. Promoting AI technologies through farmer training, subsidies, and infrastructure development can play a vital role in advancing sustainable and profitable sugarcane farming.