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
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.
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