# A New Hybrid ConvViT Model for Dangerous Farm Insect Detection
## 2 Related Works  
In this section, we reviewed and outlined some studies in the literature focused on deep learning-based harmful insect detection. [PERSON] et al. [1] indicated that dangerous insects significantly challenge crop production, necessitating the development of remedies to address this issue. The authors employed the YoloV3 method and compared it with six other algorithms. The Pest24 dataset is utilized for studies, and the mean average precision (mAP) metric is chosen as the performance criterion. The proposed approach achieved a mean average precision (mAP) of 72%. [PERSON] et al. [8] presented the detection of dangerous distant insects by applying transfer learning techniques. The authors utilized the Xception, EfficientNetB4, and InceptionResNetV2 algorithms to identify the defects. They also utilized the Dangerous Farm Insect dataset, which has 1591 images with 15 different classes. The experiments indicated that Xception achieved a validation accuracy of 83.99%, surpassing the other models. [PERSON] et al. [9] highlighted that detecting dangerous insects presents a significant challenge, necessitating more robust methods. Consequently, researchers introduced PesNet for insect identification utilizing multiclass classification through deep learning. The experiments employed the IP102 and Dangerous Farm Insect datasets. The proposed method was evaluated against four algorithms, utilizing the accuracy ratio as the performance metric. The findings indicate that the suggested algorithm surpassed the previous algorithms, achieving a maximum accuracy of 91.25% for four specified classes. [PERSON] et al. [10] employed a transfer learning method to identify dangerous insects. The Dangerous Farm Insect dataset was utilized in the experiments, and the classification problem was addressed with ResNet50V2, ResNet152V2, MobileNetV2, Xception, and traditional CNN. The results indicate that the proposed CNN model had exceptional performance, achieving 92% accuracy. [PERSON] et al. [11] introduced an innovative method for classifying dangerous insects by applying saliency maps and deep convolutional neural networks. Experimental tests were performed on a dataset called PestID, which has 12 classes, and the proposed model achieved a superior detection accuracy of 95.1% compared to five other techniques. [PERSON] and [PERSON] [12] developed an IoT-based dangerous insect identification system utilizing a weight-optimized deep neural network and YOLO techniques. The authors built a new dataset for the study and utilized it for experimental research. The Yolo algorithm was utilized for classification, with performance indicators including accuracy, sensitivity, specificity, and precision. The findings indicate that the proposed method outperformed other existing models, achieving an accuracy rate of 96.71%, 96.7% sensitivity, 96.72% specificity, and 74.67% precision. In [13], the authors introduced a deep learning-based pest identification technique and proposed a superior pest detection system. They applied Res2 Net for feature selection and employed a data augmentation algorithm. A dataset of 4000 images categorized into four classes was utilized, and experiments compared the proposed methods with current algorithms based on the results obtained. Ultimately, they achieved a mean average position score of 81%, surpassing previous algorithms. [PERSON] et al. [14] aimed to create a real-time pest detection system that balances efficiency and accuracy in agriculture. They suggested an enhanced convolutional neural network incorporating a squeeze-and-excitation attention strategy. The authors employed YoloV4 and further CNN advancements. The Pest24 dataset, comprising 28,000 images and 24 classes, was utilized in the experiments, with mAP and recall metrics employed as performance indicators. Ultimately, they determined that the proposed approach yielded a mAP of 71.6% and a recall of 83.5%.