A Framework for Crop Disease Detection Using Feature Fusion Method


  • Radhika Bhagwat Department of Technology, Savitribai Phule Pune University, Pune, India; Department of Information Technology, Cummins College of Engineering for Women, Pune, India
  • Yogesh Dandawate Electronics and Telecommunication Engineering, Vishwaskarma Institute of Information Technology, Pune, India




crop disease detection, feature fusion, convolutional neural network, hand-crafted features, cepstral coefficients


Crop disease detection methods vary from traditional machine learning, which uses Hand-Crafted Features (HCF) to the current deep learning techniques that utilize deep features. In this study, a hybrid framework is designed for crop disease detection using feature fusion. Convolutional Neural Network (CNN) is used for high level features that are fused with HCF. Cepstral coefficients of RGB images are presented as one of the features along with the other popular HCF. The proposed hybrid model is tested on the whole leaf images and also on the image patches which have individual lesions. The experimental results give an enhanced performance with a classification accuracy of 99.93% for the whole leaf images and 99.74% for the images with individual lesions. The proposed model also shows a significant improvement in comparison to the state-of-art techniques. The improved results show the prominence of feature fusion and establish cepstral coefficients as a pertinent feature for crop disease detection.


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How to Cite

R. Bhagwat and Y. . Dandawate, “A Framework for Crop Disease Detection Using Feature Fusion Method”, Int. j. eng. technol. innov., vol. 11, no. 3, pp. 216–228, Jun. 2021.