JACIII Vol.26 No.6 pp. 914-921
doi: 10.20965/jaciii.2022.p0914


fMaize: A Seamless Image Filtering and Deep Transfer EfficientNet-b0 Model for Sub-Classifying Fungi Species Infecting Zea mays Leaves

Jonnel D. Alejandrino*,†, Ronnie S. Concepcion II**, Edwin Sybingco*, Maria Gemel B. Palconit*, Mary Grace Ann C. Bautista*, Argel A. Bandala*, and Elmer P. Dadios**

*Department of Electronics and Computer Engineering, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

**Department of Manufacturing Engineering and Management, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

April 8, 2022
June 11, 2022
November 20, 2022
CNN modeling, fungi species, machine vision, maize leaf diseases, transfer learning

Identification of fungi infecting Zea mays leaves and sub-classifying them to have correct course management in the earlier stages is lucrative. To develop a nondestructive and low-cost classification model of corn leaves infected by Setosphaeria turcica (ST), Cercospora zeae-maydis (CZM), and Puccinia sorghi (PS) fungi using image filtering and transfer learning model. Corn leaf images were categorized based on fungal-infection and stored in an image library. All images were then processed to show different intensities and then utilized to filter the images. An original RGB-based CNN model has been compared with selected pre-trained models of VGG16 and EfficientNet-b0 with inputs of both unfiltered and filtered RGB images. Results showed that the EfficientNet-b0 with filtered images model (fMaize) exhibited the highest accuracy of 97.63%, sensitivity of 97.99%, specificity of 97.38, quality index of 97.68%, and F-score of 96.48%. Consequently, the experimental results revealed that deep transfer learning models fed with filtered images produced higher accuracy than models that simply employed RGB images. Thus, transfer learning was proven to be a valuable tool in enhancing CNN image classification accuracy.

Sample images of Zea mays infected leaves and the microscopic view of their fungi

Sample images of Zea mays infected leaves and the microscopic view of their fungi

Cite this article as:
J. Alejandrino, R. Concepcion II, E. Sybingco, M. Palconit, M. Bautista, A. Bandala, and E. Dadios, “fMaize: A Seamless Image Filtering and Deep Transfer EfficientNet-b0 Model for Sub-Classifying Fungi Species Infecting Zea mays Leaves,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.6, pp. 914-921, 2022.
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Last updated on May. 19, 2024