Abstract
The presence of soil moisture is of paramount importance in agricultural domain, as it constitutes largely towards variations in soil texture, and development of crops. Hence, evaluation of this parameter can turn out to be very effective while performing agricultural activities. Mainly, evaluations for the parameter is done with an aim to estimate and reduce water consumption within the fields. In this article, the presence of soil moisture has been evaluated at three different levels, i.e., 10 cm, 45 cm, and 80 cm through Autoregressive Integrated Moving Average (ARIMA) modeling technique based on Time Series Analysis, to predict the future possible values so that precise distribution water can be done within the fields. The intermediate diminution in error rates attained by the modeling technique attained between 74%–77% in comparison to other modeling techniques, depicting its superiority. Based on the results, distribution of water was scheduled in advance as per the minimal requirement, resulting lesser consumption and better crop yields
The authors would like to acknowledge Council of Scientific and Industrial Research (CSIR) for funding grants vide No. 38(1464)/18/EMIR-II for carrying out research work.
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Khanna, A., Kaur, S. (2021). Evaluation of Soil Moisture for Estimation of Irrigation Pattern by Using Machine Learning Methods. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-030-88244-0_33
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