  India's agricultural system has been facing a severe problem of crop wastage.
A key contributing factor to this problem is that many small farmers lack
access to reliable cold storage that extends crop shelf-life. To avoid having
leftover crops that spoil, these farmers often sell their crops at unfavorable
low prices. Inevitably, not all crops are sold before spoilage. Even if the
farmers have access to cold storage, the farmers may not know how long to hold
different crops in cold storage for, which hinges on strategizing over when and
where to sell their harvest. In this note, we present progress toward a
simultaneous hardware and software solution that aims to help farmers reduce
crop spoilage and increase their profits. The hardware is a cost-effective
solar-powered refrigerator and control unit. The software refers to a produce
price forecasting system, for which we have tested a number of machine learning
methods. Note that unlike standard price forecasting tasks such as for stock
market data, the produce price data from predominantly rural Indian markets
have a large amount of missing values. In developing our two-pronged solution,
we are actively working with farmers at two pilot sites in Karnataka and
Odisha.
