AI-based Market Intelligence Systems for Farmer Collectives: A Case Study from India

Abstract

Small and marginal farmers are unable to get a good price for their produce, because they face several challenges in market participation. Aggregation of produce via farmer cooperatives and the ability to delay sales (for non-perishable crops) to when market prices are high has emerged as a useful strategy to improve farmer incomes. We work with a network of farmer cooperatives in India growing soyabean and explore the potential of developing a machine learning–based price forecasting and sales recommendation system that produces suggestions on the best dates when harvested soyabean crops should be sold, e.g., whether to sell right away (if prices are likely to fall in the future) or to wait (if prices are likely to rise). We present an evaluation of different methods for price forecasting and a prospect theory–based method to produce sales recommendations. Experiments on historical data indicate that we can provide modest gains to farmers, and we build and field test an Android application for this purpose. Early results indicate positive feedback. Our methods can be generalized to other agricultural commodities that can be stored for several months and help farmer cooperatives to compete effectively in agricultural markets.