Authors: Vishnu K S, Dr. Neelakanta N T
Abstract: Plantations, which include tea, coffee, rubber, and spices, are a vital component of the Indian economy, providing significant export revenue and supporting the livelihoods of millions of people in ecologically fragile regions. Additionally, agriculture is another important sector in India. Even so, the sector is now confronted with mounting tensions: fluctuating prices, erratic yields caused by climate change, and a complex global supply chain. Traditional market intelligence tools, which rely on outdated data and manual reporting, are no longer sufficient in the fast-paced digital world. How can Artificial Intelligence (AI) change market intelligence for India's plantation crops and help the industry adapt more efficiently? By utilizing data from the Coffee Board of India (2023), the Tea Board, and recent Agritech experiments, the study highlights the effectiveness of machine learning with predictive analytics and Natural Language Processing (NLP) in forecasting market changes. An integrated AI system that combines global trade signals, satellite-based crop health indicators and local auction results offers precise insights for farmers cooperatives, exporters, and other stakeholders. The plan is practical.".According to the results, advanced AI tools are primarily being used on large estates, which make up around 15% of such units, while smallholder cooperatives have been left out. By spreading the word, a reduction of up to 12% in the gap between small growers' earnings and the market's value could be achieved. The study highlights significant obstacles such as disorganized and fluctuating data, expensive local AI infrastructure, and limited digital proficiency, while proposing a gradual improvement to make these technologies more accessible. According to the paper, India's plantation industry must rely on AI-driven market intelligence to transition from a passive price payer to an informed player in global markets. This is not optional but crucial.
DOI: https://doi.org/10.5281/zenodo.20107791
