Patel, R and Enaganti, I and Bhardwaj, MR and Narahari, Y (2022) A Data-Driven, Farmer-Oriented Agricultural Crop Recommendation Engine (ACRE). In: 10th International Conference on Big Data Analytics, BDA 2022, 19 - 22 December 2022, Hyderabad, 227 – 248.
Full text not available from this repository.Abstract
Agriculture has a significant role to play in any emerging economy and provides the source of income and employment for a large portion of the population. A key challenge faced by small and marginal farmers is to determine which crops to grow to maximize their utililty. With a wrong choice of crops, farmers could end up with sub-optimal yields and low, and possibly even loss of revenue. This work seeks to design and develop ACRE (Agricultural Crop Recommendation Engine), a tool that provides a scientific method to choose a crop or a portfolio of crops, to maximize the utility to the farmer. ACRE uses available data such as soil characteristics, weather conditions, and historical yield data, and uses state-of-the-art machine learning/deep learning models to compute an estimated utility to the farmer. The main idea of ACRE is to generate several recommendations of portfolios of crops, with a ranking of portfolios based on the Sharpe ratio, a popular risk metric in financial investments. We use publicly available data from agmarknet portal in India to perform several thought experiments with ACRE. ACRE provides a rigorous, data-driven backend for designing farmer-friendly mobile apps for assisting farmers in choosing crops (This work was supported by the National Bank for Agriculture and Rural Development (NABARD), Government of India, through a research grant). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | All rights are reserved for Springer Science and Business Media Deutschland GmbH. |
| Keywords: | Crops; Deep learning; Economics; Engines; Learning systems; Recommender systems; Regional planning; Risk assessment; Risk perception; Agricultural crops; Crop portfolio; Crop recommendation; Data driven; Deep learning; Emerging economies; Machine-learning; Scientific method; Sharpe ratios; Yield estimation; Risk analysis |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 12 Mar 2023 05:23 |
| Last Modified: | 12 Mar 2023 05:23 |
| URI: | https://eprints.iisc.ac.in/id/eprint/80924 |
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