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An efficient densely connected convolutional neural network for identification of plant diseases

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Abstract

In this research work, novel densely connected convolutional neural network (DCCNN) based deep learning architectures are proposed to identify diseases in the apple, corn, cucumber, grape and potato plant leaves. The three concrete novel deep learning architectures, namely 6 block DCCNN, 7 block DCCNN and 8 block DCCNN are compared with state-of-the-art conventional machine learning and deep learning approaches. The performance is evaluated using training accuracy, validation accuracy, loss values, confusion matrices, sensitivity, specificity, precision and F-score measures. The 8 block DCCNN achieved greater identification accuracy of 99.78%, 98.85%, 98.23%, 99.78, % and 99.83% for apple, corn, cucumber, grape and potato plant leaf dataset respectively. The higher identification accuracy is achieved in the proposed 8 block DCCNN since the densely connected convolution neural network layers are incorporated with modified dense blocks.

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Acknowledgements

The authors would like to thank Dr. Shanwen Zhang, Department of Electronics and Information Engineering, XiJing University, Xi’an 710123, China for providing the access to use the cucumber leaf image database. Sincere thanks to Dr. S Domnic, Artificial Intelligence, Center of Excellence, Department of Computer Applications, National Institute of Technology Trichy, India for providing access to use the GPU workstation for simulations.

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  1. Department of Electronics and Communication Engineering, National Institute of Technology Trichy, Tiruchirappalli, India

    G. Yogeswararao, V. Naresh, R. Malmathanraj & P. Palanisamy

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  1. G. Yogeswararao
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  2. V. Naresh
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Correspondence to G. Yogeswararao.

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Yogeswararao, G., Naresh, V., Malmathanraj, R. et al. An efficient densely connected convolutional neural network for identification of plant diseases. Multimed Tools Appl 81, 32791–32816 (2022). https://doi.org/10.1007/s11042-022-13053-1

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