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- W4320151738 abstract "AbstractAgriculture 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).KeywordsCrop recommendationCrop portfolioYield estimationMachine learningDeep learningSharpe ratioRisk analysis" @default.
- W4320151738 created "2023-02-13" @default.
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- W4320151738 date "2022-01-01" @default.
- W4320151738 modified "2023-09-27" @default.
- W4320151738 title "A Data-Driven, Farmer-Oriented Agricultural Crop Recommendation Engine (ACRE)" @default.
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- W4320151738 doi "https://doi.org/10.1007/978-3-031-24094-2_16" @default.
- W4320151738 hasPublicationYear "2022" @default.
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