Open AccessClassification of Diseases in Paddy using Deep Convolutional Neural Network
Author(s) -
V. Malathi,
M. P. Gopinath
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1964/4/042028
Subject(s) - convolutional neural network , support vector machine , artificial intelligence , computer science , deep learning , artificial neural network , word error rate , field (mathematics) , pattern recognition (psychology) , machine learning , mathematics , pure mathematics
Paddy disease detection is decisive in the field of automatic pathogens diagnosis machine. Currently, Deep Con- volutional neural network typically examined the state-of-the art results in image classification. In this work, we proposed a novel DCNN model to identify previously known bacteria leaf blight, brown spot, leaf blast, leaf smut and narrow diseases in prior knowledge. A unique repository of data holds 1260 images of different diseases, 80% of data carried out for training and 20% for testing the samples. To add advantages to our model, we built our model using ADAM optimizer and conducted comparative research over SVM (support vector machine), KNN (K-Nearest neighbor) and ANN (Artificial Neural Network). The dataset given to the novel DCNN model with keras framework and achieved testing accuracy of 0.940 with less training error rate of 0.013. The interpretation outcome demonstrates that high level image classification accuracy with less error rate was achieved by novel DCNN model than traditional methods. Therefore, our model performs best for recognizing 5 paddy diseases and can be possibly implemented in day to day life application.