## 6 Conclusions  
This study presents a novel approach for detecting and assessing _Zyginidia pullula_-induced damage in maize crops by leveraging both deep learning and traditional feature extraction techniques. By integrating CNN-based models (ResNet50, DenseNet-201, and EfficientNetB2) with handcrafted features (Gabor filters, GLCM, and HSV), a robust feature representation was achieved, enabling the effective classification of infected maize leaves. The experimental results demonstrate that hybrid feature combinations significantly enhance classification accuracy compared to individual feature sets. Notably, the combination of ResNet50, HSV, GLCM, and Gabor, when paired with an ANN classifier, achieved the highest accuracy of 92.55%, underscoring the importance of multi-feature integration in pest damage detection.  
Furthermore, a detailed analysis of infection severity levels was conducted, where the combination of ResNet50, Gabor, and HSV achieved an accuracy of 83.78% with ANN, highlighting the effectiveness of hybrid feature fusion in classifying infection intensity. These findings emphasize the advantages of integrating both deep and traditional feature extraction methods for automated pest damage detection in agricultural applications.  
Unlike prior studies that primarily focus on plant disease classification, this research introduces an innovative approach for quantifying and classifying the damage caused by a specific pest, _Zyginidia pullula_, on maize leaves. To the best of our knowledge, no prior research systematically evaluates the severity of pest-induced damage using a combination of deep learning-based and handcrafted features. The proposed methodology bridges this gap by offering an automated and accurate framework for both binary classification (healthy vs. infected) and severity assessment of infection levels. The results strongly support the potential of hybrid feature extraction in enhancing classification accuracy, demonstrating that integrating multiple feature representations leads to improved pest damage detection.  
Future research may focus on extending this approach to other pest species, incorporating larger and more diverse datasets, and optimizing computational efficiency for real-time agricultural applications. Additionally, to enhance scalability in field conditions, future work may explore UAV-based automated image collection pipelines.  
Conceptualization, A.S.; methodology, H.E.B. and A.S.; software, H.E.B. and C.B.; validation, H.E.B.; formal analysis, H.E.B.; investigation, H.E.B.; resources, H.E.B.; data curation, H.E.B. and C.B.; writing--original draft preparation, H.E.B.; writing--review and editing, C.B. and A.S.; visualization, H.E.B. and C.B.; supervision, A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** The authors would like to thank The Scientific and Technological Research Council of Turkiye (TUBITAK) for their support to the research presented in this study 2211-C National Ph.D. Scholarship Program. The authors thank Professor [PERSON] for their valuable support in the dataset labeling process, which significantly contributed to this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.