Volume 18, No. 6, 2021
Artificial Wavelet Neural Network For Paddy Crop Yield Prediction
M.Sivasubramanian , S. Meenakshi , V. Prema
Abstract
India’s economy is based primarily on agriculture. Agronomic yield is influenced by organic, economic, and seasonal factors. As a result, crop yield estimation is a very difficult task. Numerous investigations have been conducted in agriculture to forecast crop yield using different techniques ranging from statistical to machine learning models. This article intends to propose a reliable and accurate model to perform paddy crop yield prediction. Data pertaining to the paddy crop are collected and then preprocessed. Two neural networks namely Feed Forward Neural Network (FFNN) and Wavelet Artificial Neural Network (WANN) arebuilt and trained using the preprocessed data to anticipate paddy crop yield.A few metrics are employed to assess and compare the efficacy of the designed models. Numerical results demonstrated that the developed model, WANNoffers higher performance than that of FFNN model as well as past approaches reported in the literature.
Pages: 9991-10000
Keywords: Crop yield,feed forward neural network,paddy, prediction.and wavelet artificial