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An efficient crop identification using deep learning
N. Agila,
Published in International Journal of Scientific and Technology Research
Volume: 9
Issue: 1
Pages: 2805 - 2808
In modern era, the deep neural network is the prominent tool in agricultural industry for providing support to farmers in monitoring crop yield based on the weather conditions. In this paper, the recurrent neural network is utilized for detecting the suitable crop for the observed environmental conditions from the field and also provides the suggestions about the desired crop can be grown in that field or not. The environmental parameters such as humidity, temperature, rain and moisture are obtained through the sensors and fed as input to recurrent neural network. Then, the recurrent neural network identifies the suitable crop by classifying the crop based on the climatic conditions. The experiment was conducted by using the Random Forest classifier, Decision Tree classifier, Logistic Regression, Support Vector Machine classifier (SVM), Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN). The result shows that the recurrent neural network outperforms other methodologies. © IJSTR 2020.
About the journal
JournalInternational Journal of Scientific and Technology Research
PublisherInternational Journal of Scientific and Technology Research