Open Access

ARTICLE

Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model

by S. Muthukumaran^1,*, P. Geetha², E. Ramaraj¹

1 Department of Computer Science, Alagappa University, Karaikudi, 630003, India
2 Department of Computer Science, Dr. Umayal Ramanathan College for Women, Karaikudi, 630003, India

* Corresponding Author: S. Muthukumaran. Email:

Intelligent Automation & Soft Computing 2023, 35(1), 215-230. https://doi.org/10.32604/iasc.2023.027449

Received 18 January 2022; Accepted 21 February 2022; Issue published 06 June 2022

Abstract

Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth. Rice is propagated from the seeds of paddy and it is a stable food almost used by fifty percent of the total world population. The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains. This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques. Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy. There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind, relative humidity, Instant Wind Speed in paddy cultivation. The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series. A Robust Optimized Artificial Neural Network (ROANN) Algorithm with Genetic Algorithm (GA) and Multi Objective Particle Swarm Optimization Algorithm (MOPSO) proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation. A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database. The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database. Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm. The reason for improving the growth of paddy was identified using the output of the Neural Network. Performance metrics such as Accuracy, Error Rate etc were used to measure the performance of the proposed algorithm. Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.

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Keywords

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