Toward Reducing Crop Spoilage and Increasing Small Farmer Profits in India: a Simultaneous Hardware and Software Solution

TL;DR

This paper presents a combined hardware and software approach to reduce crop spoilage and boost profits for small Indian farmers by providing solar-powered refrigeration and a machine learning-based price forecasting system.

Contribution

It introduces a cost-effective solar refrigerator and a novel produce price forecasting system tailored for rural Indian markets with missing data.

Findings

Successful testing of machine learning methods for price prediction.

Implementation of a solar-powered refrigerator prototype.

Active collaboration with farmers at pilot sites.

Abstract

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.