Deep Neural Network and Multiparametric Deep Neural Networks for Harvest Predictions

S. Ashok Kumar¹ , S. Anusya²

1. School of CS, A.V.P. College of Arts and Science, Tirupur, Tamilnadu, India.
2. School of CS, A.V.P. College of Arts and Science, Tirupur, Tamilnadu, India.

Section:Research Paper, Product Type: Journal-Paper
Vol.12 , Issue.6 , pp.1-5, Dec-2024

Online published on Dec 31, 2024

Copyright © S. Ashok Kumar, S. Anusya . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Deep Neural Network is a Deep Learning technique which was used for predicting the crop yields. To forecast various crop yields, the Deep Neural Network(DNN) used a series of non-linear layers, which, at each level, abstracted the original meteorological and soil data. The paper proposes use of the Multiparametric Deep Neural Network(MDNN) to simulate various soil properties, climatic variables such as temperature, vapour pressure, cloud coverage, frequency of the damp days and humidity and other climatic variations in order to forecast the agricultural yields outperforming the existing DNN. In order to improve the accuracy the MDNN uses the data from the past. This paper presents an improvement of accuracy of the harvest predictions which will help farmers and officials which aid for better yield. Results from the experiments show that in predicting the agricultural yields of the selected crops the MDNN performs better than the DNN.

Key-Words / Index Term :
Agriculture, Crop Yield, Deep Neural Network, Harvest, Hyperparameters, Multiparametric Neural Networks

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