Abstract
India Meteorological Department (IMD) is delivering agromet advisories, i.e., weather-based crop risk management advisories based on the medium-range weather forecast (five days) across India. Based on the weather prediction, once in five days, agromet advisory is provided for major crops and livestock by considering the district/block as a unit. In the literature, a framework was proposed to improve the process of advisory preparation by employing the notion of reuse. In that framework, an approach was explored to reuse the advisory prepared for the given weather situation to prepare advisory for similar weather situations in the future. For this, a notion of category-based weather condition (CWC) was proposed to model a given weather situation. The experiments conducted by comparing CWCs of weather situations over a period of time showed a significant improvement in reuse. In this paper, we have conducted a validation study to analyze the scope of reuse by comparing the advisory text of the corresponding weather situations. The experiments on agromet advisory text data related to the Rice crop delivered from 2016 to 2019 for Telangana State show that if the advisory texts are similar, there is a high probability that the corresponding CWCs are also similar. The results validate that the CWC-based reuse framework can be employed to exploit reuse across weather situations.
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Acknowledgement
This work is supported by India-Japan Joint Research Laboratory Project entitled “Data Science based farming support system for sustainable crop production under climatic change (DSFS)”, funded by Department of Science and Technology, India (DST) and Japan Science and Technology Agency (JST). We would also like to thank India Meteorological Department for providing the dataset for the experiments.
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Alugubelly, M., Polepalli, K.R., Mondal, A., Mahadevappa, S.G., Banoth, B.N., Gade, S. (2022). Analysis of Weather Condition Based Reuse Among Agromet Advisory: A Validation Study. 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_18
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