Plant disease prediction using convolutional neural network

  • Hema M S Department of Information Technology Anurag University, Hyderabad, India
  • †, Niteesha Sharma2 Department of Information Technology Anurag University, Hyderabad, India
  • Y Sowjanya Department of Information Technology Anurag University, Hyderabad, India
  • Ch. Santoshini Department of Information Technology Anurag University, Hyderabad, India
  • R Sri Durga Department of Information Technology Anurag University, Hyderabad, India
  • V. Akhila Department of Information Technology Anurag University, Hyderabad, India
Keywords: Plant disease, Convolutional neural network, VGG16, Resnet34, leaf image


Every year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely feature extraction, downsizing image and classification. In CNN, the convolutional layer extracts the feature from plant image. The pooling layer downsizing the image. The disease classification was done in dense layer. The proposed model can recognize 38 differing types of plant diseases out of 14 different plants with the power to differentiate plant leaves from their surroundings. The performance of VGG16 and Resnet34 was compared.  The accuracy, sensitivity and specificity was taken as performance Metrix. It helps to give personalized recommendations to the farmers based on soil features, temperature and humidity


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How to Cite
M S, H., †, Niteesha Sharma, Y Sowjanya, Ch. Santoshini, R Sri Durga, & V. Akhila. (2021). Plant disease prediction using convolutional neural network. EMITTER International Journal of Engineering Technology, 9(2), 283-293.