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multi-objective optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model

Intelligent Automation & Soft Computing 2023年第1期35卷 215-230页

作者： S.Muthukumaran P.Geetha E.Ramaraj Department of Computer Science Alagappa UniversityKaraikudi630003India Department of Computer Science Dr.Umayal Ramanathan College for WomenKaraikudi630003India

Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the *** is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world *** 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 *** paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation *** of the researchers used to relay on historical records of meteorological parameters to predict the yield of *** 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 *** 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 *** input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the *** database optimized by using Artificial Neural Network Back Propagation *** reason for improving the growth of paddy was identified using the output of the Neural *** metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed *** analysis made between ANN with GA and ANN with MOPSO to identify the recommenda

关键词： ANN back propagation algorithm genetic algorithm multi objective particle swarm optimization algorithm

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