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Rice-net: an efficient artificial fish swarm optimization applied deep convolutional neural network model for identifying the Oryza sativa diseases

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Abstract

This research aims to identify rice diseases, namely Leaf blast, Brown spot, Healthy and Hispa. The purpose of this research is to utilize deep convolutional neural network (DCNN) with support vector machine (SVM), DCNN with artificial neural network (ANN) and DCNN with long short-term memory (LSTM). To enhance the performance of LSTM further, the research includes particle swarm optimization, artificial fish swarm optimization (AFSO) and efficient artificial fish swarm optimization (EAFSO) to identify optimal weights. This research also compares the proposed technique results with a conventional feature extraction approaches like texture, discrete wavelet transforms and color histogram with SVM, ANN and LSTM. The results exhibit the superiority of proposed DCNN-LSTM (EAFSO) technique over other techniques. The proposed technique EAFSO associates DCNN-LSTM identifies the rice diseases with 97.5% accuracy, which is better than DCNN-SVM and DCNN-ANN.

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Authors and Affiliations

  1. Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India

    N. V. Raja Reddy Goluguri & K. Suganya Devi

  2. Information Technology, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, 535005, India

    N. V. Raja Reddy Goluguri

  3. Department of Physics, National Institute of Technology Silchar, Silchar, Assam, 788010, India

    P. Srinivasan

Authors
  1. N. V. Raja Reddy Goluguri
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  2. K. Suganya Devi
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  3. P. Srinivasan
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Correspondence to K. Suganya Devi.

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All authors have taken part in the analysis and interpretation of the information, drafting the article for significant scholarly substance. This manuscript has not been submitted to, nor is under survey at, another journal or other publishing venue. The authors have no connection with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. The authors declare that they have no conflict of interest.

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Goluguri, N.V.R.R., Devi, K.S. & Srinivasan, P. Rice-net: an efficient artificial fish swarm optimization applied deep convolutional neural network model for identifying the Oryza sativa diseases. Neural Comput & Applic 33, 5869–5884 (2021). https://doi.org/10.1007/s00521-020-05364-x

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