International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Optimized Deep Learning Techniques for Disease Detection in Rice Crop using Merged Datasets

  • Muhammad Junaid (Department of Computer Science, University of Engineering and Technology) ;
  • Sohail Jabbar (Department of Computer Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)) ;
  • Muhammad Munwar Iqbal (Department of Computer Science, University of Engineering and Technology) ;
  • Saqib Majeed (University institute of Information Technology, PMAS-Arid Agriculture University) ;
  • Mubarak Albathan (Department of Computer Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)) ;
  • Qaisar Abbas (Department of Computer Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)) ;
  • Ayyaz Hussain (Department of Computer Science, Quaid-i-Azam University)
  • Received : 2023.03.05
  • Published : 2023.03.30
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Abstract

Rice is an important food crop for most of the population in the world and it is largely cultivated in Pakistan. It not only fulfills food demand in the country but also contributes to the wealth of Pakistan. But its production can be affected by climate change. The irregularities in the climate can cause several diseases such as brown spots, bacterial blight, tungro and leaf blasts, etc. Detection of these diseases is necessary for suitable treatment. These diseases can be effectively detected using deep learning such as Convolution Neural networks. Due to the small dataset, transfer learning models such as vgg16 model can effectively detect the diseases. In this paper, vgg16, inception and xception models are used. Vgg16, inception and xception models have achieved 99.22%, 88.48% and 93.92% validation accuracies when the epoch value is set to 10. Evaluation of models has also been done using accuracy, recall, precision, and confusion matrix.

Keywords

Acknowledgement

The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) for funding and supporting this work through Research Partnership Program no. RP-21-07-11.

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