# A New Hybrid ConvViT Model for Dangerous Farm Insect Detection
## 2 Related Works
The authors of [15] proposed a pest detection system utilizing EfficientNetB3 and developed a strategy employing data augmentation techniques. The authors utilized a dataset with 350 images and nine classes in the tests. They employed accuracy, precision, recall, and F1 metrics as performance indicators. The results demonstrated a high accuracy rate of 97.78%, 97.80% precision, 97.78 recall, and a 97.75% F-score. [PERSON] et al. [16] addressed the issue of identifying tiny insect pests. They presented a technique utilizing the YoloV3 and Xception algorithms. The authors utilized a real-world pest image dataset and employed precision, recall, and F-scores as performance indicators. The results indicate that the proposed method achieved a precision of 77%, a recall of 36%, and an F-score of 50%. In [17], the authors aimed to identify agricultural pests using an anchor-free methodology. They utilized the Fast R-CNN method in conjunction with feature fusion models. The experimental studies utilized a dataset comprising 20,000 images with 24 classes. Mean average precision (mAP) and recall as performance metrics yielded 56.4% mAP and 85.1% recall findings. In [18], the authors presented a classification system for mango pests and introduced a novel strategy utilizing entropy ELM with the whale optimization algorithm. The experiments employed high-dimensional data comprising images of mango pests. Recall, precision, and F-scores were used as performance indicators. The results indicate that the employed approach achieved 89.8% precision, 98.5% recall, and a 99.8% F-score. [PERSON] et al. [19] aimed to employ transfer learning for effective pest identification. They presented a model utilizing EfficientNetB0 for insect detection. They utilized a customized insect dataset and compared their approach with support vector machines, artificial neural networks, and multiple kernel learning. The results indicate that their proposed algorithm achieved a superior accuracy rate of 97.51%. [PERSON] et al. [20] proposed a method for detecting and classifying thrips in strawberry plants. They suggested an approach incorporating image processing with SVM classifiers with customized kernels. A dataset comprising images of strawberry pests was utilized, with MSE, RMSE, MPE, and MAE employed as performance indicators. The minimum error rates achieved in the experimental studies were MSE 0.090, RMSE 0.300, MAE 0.045, and MPE 2.25. Table 1 compares previous studies on dangerous insect detection. As observed in the reviewed works, deep learning models such as classical CNNs, transfer learning, and YOLO-based approaches have been extensively used for insect classification and detection. Most studies rely on accuracy as the primary performance indicator, with relatively less emphasis on generalizability across diverse datasets and robustness against varying environmental conditions (e.g., lighting, occlusions, and insect size variations). Furthermore, while vision transformers (ViTs) have demonstrated significant advancements in various computer vision tasks, their application to insect detection remains limited. Existing approaches focus on either CNN-based architectures or hybrid deep learning techniques, lacking an effective integration of CNNs with transformer-based models. This gap in the literature motivated us to develop a CNN-based hybrid vision transformer model for dangerous insect detection, leveraging the strengths of both architectures. Our proposed model enhances detection accuracy and improves robustness and generalization across different datasets, surpassing the performance of models such as EfficientNetB0, DenseNet201, ResNet-50, VGG-16, and standalone ViTs.