## 2 Related Works
Also, combining models can reduce the risk of identification errors and improve the system's robustness. This improvement is crucial inagricultural environments where conditions are dynamic and variable. In a recent study, the authors [20] discussed the development of an agricultural pest image classifier using a ResNet-based matching network and an NT-Xent loss function. The article addressed a major limitation of classical methods by proposing the detection of new classes of pests without retraining the network. This is because the features learned previously can be applied to new classes. Removing fully connected layers of the network did not require fine retraining when a new class appeared. The results demonstrated a classification accuracy of 84.29% for the D0 dataset. The potential of DL techniques based on a CNN ensemble for improving pest detection was emphasized in [21]. The study introduced notable ensemble techniques based on NMS, SoftNMS, weighted box fusion (WBF), and non-maximum weighted (NMW) in this regard. The results showed that the WBF approach was valuable and improved the mAP by 20%. In addition, the study used a dataset constructed from images taken from real conditions, finally noting the relevance and applicability of such methods for advanced detection systems in real agricultural scenarios. Another modern combining strategy was proposed as an effective pest classification method by combining multiple CNN models based on improved transformer architectures [22]. The proposed DenseNet vision transformer system efficiently combined the local and global features of the chosen models by using an ensemble voting algorithm. The authors' experiments were performed on the public datasets D0 and IP102 and demonstrated remarkable accuracy in both cases of 99.89% and 74.2%, respectively. The article highlighted a promising method for accurately identifying numerous insect pests, outperforming other state-of-the-art methods. The power of combining EfficientNet CNNs to improve large-scale pest image classification was highlighted in [15]. The combination model was based on fine-tuning and SVM techniques, proposing an approach that replaces the traditional SoftMax function with a PM-SVM one, while focusing on reducing margin-based loss instead of cross-entropy loss. The experimental results showed remarkable accuracy for the considered datasets D0 and IP102 of 99% and 72.31%, respectively. These obtained metrics underline the effectiveness of the proposed method, providing a high-performance solution for applications in smart agriculture and pest monitoring. The authors of [23] presented an innovative approach for pest classification in agricultural areas using a weighted ensemble of CNNs, optimized with genetic algorithms. In this study, seven pretrained models were targeted, trained, and optimized using transfer learning strategies on the D0 dataset. The best models were selected (MobileNet, Xception, and InceptionV3) and combined using a weighted voting method based on genetic algorithms. The experimental results reached a classification accuracy of 98.81%, demonstrating the effectiveness of the proposed method in terms of performance and robustness. It has been observed that the implementation of a weighted ensemble of CNNs provides a valuable, efficient, and adaptable solution for accurate pest identification under various agricultural conditions. A novel learning assistance method that enhances CNN-based models for insect pest classification using a two-step training strategy is proposed in [24]. The \"Deep\" step focuses on improving feature discrimination using triplet margin loss, while the \"Wide\" step enhances generalization with Mixup augmentation. This approach is on IP102 and D0 datasets and improved efficiency and adaptability. The new study [25] introduced a hybrid deep learning architecture combining CNNs and Transformers, integrating two key modules: a Feature Fusion Module for multi-scale fine-grained feature extraction and a Mixed Attention Module for modeling long-range dependencies.