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Small and marginal farmers, who account for over 80% of India's agriculturalpopulation, often sell their harvest at low, unfavorable prices beforespoilage. These farmers often lack access to either cold storage or marketforecasts. In particular, by having access to cold storage, farmers can storetheir produce for longer and thus have more flexibility as to when they shouldsell their harvest by. Meanwhile, by having access to market forecasts, farmerscan more easily identify which markets to sell at and when. While affordablecold storage solutions have become more widely available, there has been lesswork on produce price forecasting. A key challenge is that in many regions ofIndia, predominantly in rural and remote areas, we have either very limited orno produce pricing data available from public online sources.  In this paper, we present a produce price forecasting system that pulls datafrom the Indian Ministry of Agriculture and Farmers Welfare's websiteAgmarknet, trains a model of prices using over a thousand markets, and displaysinterpretable price forecasts in a web application viewable from a mobilephone. Due to the pricing data being extremely sparse, our method first imputesmissing entries using collaborative filtering to obtain a dense dataset. Usingthis imputed dense dataset, we then train a decision-tree-based classifier topredict produce prices at different markets. In terms of interpretability, wedisplay the most relevant historical pricing data that drive each forecastedprice, where we take advantage of the fact that a wide family ofdecision-tree-based ensemble learning methods are adaptive nearest neighbormethods. We show how to construct heuristic price uncertainty intervals basedon nearest neighbors. We validate forecast accuracy on data from Agmarknet anda small field survey of a few markets in Odisha.

An Interpretable Produce Price Forecasting System for Small and Marginal  Farmers in India using Collaborative Filtering and Adaptive Nearest Neighbors