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).
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Notes
- 1.
A convex combination of numbers is a weighted sum of the numbers with weights in the range [0, 1] and the weights summing to 1.
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Acknowledgment
We gratefully acknowledge the support provided by the National Bank for Agriculture and Rural Development (NABARD), Government of India, through a research grant for carrying out this work. We must thank the discussions with Prof. Lalith Achot and Prof Vedamurthy in getting several of our questions clarified in crop recommendation. The third author (Mayank Ratan Bhardwaj) wishes to thank the Ministry of Education, Government of India, for providing the Doctoral Fellowship.
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Patel, R., Enaganti, I., Bhardwaj, M.R., Narahari, Y. (2022). A Data-Driven, Farmer-Oriented Agricultural Crop Recommendation Engine (ACRE). In: Roy, P.P., Agarwal, A., Li, T., Krishna Reddy, P., Uday Kiran, R. (eds) Big Data Analytics. BDA 2022. Lecture Notes in Computer Science, vol 13773. Springer, Cham. https://doi.org/10.1007/978-3-031-24094-2_16
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