Prediction of Mango Production Using Machine Intelligence Techniques: A Case Study from Karnataka, India

Santosha Rathod¹, Vijayakumar S¹*, Nirmala Bandumula² and Gayathri Chitikela³

¹Scientist, ICAR-Indian Institute Rice Research, Hyderabad, Telangana, India
²Senior Scientist, ICAR-Indian Institute Rice Research, Hyderabad, Telangana, India
³Professor Jayashankar Telangana State Agricultural University, Hyderabad, Telangana, India

*Corresponding Author: Vijayakumar S, Scientist, ICAR-Indian Institute Rice Research, Hyderabad, Telangana, India.

Received: July 15, 2022; Published: August 05, 2022

Abstract

Mango is the largest producing fruit crop in India. On the other hand, Karnataka is called the horticultural state of India, where mango is the highest producing fruit crop. A developing economy relies heavily on forecasting for effective planning and long-term sustainable growth. The most common technique used for forecasting in several fields for many years is autoregressive integrated moving average (ARIMA). The assumptions of linearity and stationarity are major key flaws in this model. As many time series phenomenon in the real world are not purely linear, therefore there is an opportunity to enhance the prediction ability of ARIMA models by employing nonlinear machine intelligence techniques like Autoregressive Neural Network (NAR: Neural Network Autoregressive) and non-linear support vector regression (NLSVR) model. In this study, an attempt is made to forecast the mango production of Karnataka using ARIMA, NAR and NLSVR. According to empirical evidence, the predicting accuracy of the time series machine intelligence technique is clearly superior than the traditional ARIMA model. 

 

Keywords: Mango Production; Time Series; ARIMA; NAR; NLSVR

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Citation

Citation: Santosha Rathod., et al. “Prediction of Mango Production Using Machine Intelligence Techniques: A Case Study from Karnataka, India". Acta Scientific Agriculture 6.9 (2022): 16-22.

Copyright

Copyright: © 2022 Vijayakumar S., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.