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In recent days, the crop yield prediction is a major area of research, where the information about the suitable crop to cultivate will be very much useful for the farmers to cultivate. The crop yield prediction in agricultural helps the farmers to know how much yield they can expect from the cultivation. It also helps in minimizing the loss to the farmers when unfavorable condition occurs. The proposed work is to predict the yield of the crop based on the suitable crop parameters like Temperature Min, Temperature Max, Humidity, Wind speed, Pressure using neural network model. In this research paper, crop yields predictions were established using Feed Forward Neural Network and Recurrent Neural Network model which predict the crop yield. The performances of neural network models were evaluated using the metrics like Root Mean Square Error (RMSE) and Loss.
Keywords:
Root Mean Square Error (RMSE)
Cite Article:
"PREDICTION OF CROP YIELD USING RNN, FEED FORWARD AND LSTM NEURAL NETWORKS ALGORITHMS", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 8, page no.1476 - 1484, August-2022, Available :http://www.ijrti.org/papers/IJRTI2208237.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator