An interpretable produce price forecasting system for small and marginal farmers in India using collaborative filtering and adaptive nearest neighbors
Open accessAbstract
Small and marginal farmers, who account for over 80% of India's agricultural population, often sell their harvest at low, unfavorable prices before spoilage. These farmers often lack access to either cold storage or market forecasts. In particular, by having access to cold storage, farmers can store their produce for longer and thus have more flexibility as to when they should sell their harvest by. Meanwhile, by having access to market forecasts, farmers can more easily identify which markets to sell at and when. While affordable cold storage solutions have become more widely available, there has been less work on produce price forecasting. A key challenge is that in many regions of India, predominantly in rural and remote areas, we have either very limited or no produce pricing data available from public online sources.
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ML can guide crop yield prediction and macroeconomic models that help farmers predict crop demand and decide what to plant at the beginning of the season.
“ML can guide crop yield prediction [866] and even macroeconomic models that help farmers predict crop demand and decide what to plant at the beginning of the season [503].”
From Tackling climate change with machine learning (2023) · cites this paper as [503]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Ma, W., Nowocin, K., Marathe, N., & Chen, G. H.
- Affiliations
- Carnegie Mellon University (US)
- CoolCrop
- Venue
- Proceedings of the Tenth International Conference on Information and Communication Technologies and Development
- Publisher
- ACM
- Published
- 2019-01-04
- Pages
- 1-11
- OpenAlex
- W2903854080
- Cited by
- 21
- References
- 15
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 3 · 2021: 1 · 2020: 4 · 2019: 1
- FWCI
- 4.30
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on Information and Communication Technologies and Development (ICTD) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Carnegie Mellon University
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economics of Agriculture and Food Markets
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- smallholder farmers, cold storage, market forecast, collaborative filtering, model interpretability
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