## 2 Related Studies
### Pest Detection and Classification Frameworks  
The lightweight convolutional neural network-embedded attention mechanism characterizes the method indicated in [9], which adopts EfficientNetV2 as a backbone. An efficient attention module is designed by the authors for acquiring channel relationship information and pest positional data from images under testing. However, in [10], the optimization of MobileNetV2 is performed by implementing a dynamic learning rate, freezing layers, and sparse regularization in combination with CutMix augmentation. The MobileNet is also used in [11] as the backbone network for the development of a pest detection system which is based on the Faster Region-based Convolutional Network Method. In the conceived procedure, the ResNet is substituted by the MobileNet to reduce the number of specifications because of the depth-wise separable convolutions used for the MobileNet design. Furthermore, the authors in [12; 13] use different convolutional neural network models as classifiers. In particular, pest recognition is carried out after having applied saliency methods for data augmentation in [12], while in [13], transfer learning, fine tuning and CutMix enhancement methods are performed before the classification phase. New residual structures are presented in [14; 15; 16] based on the fusion feature from previous layers. In particular, [PERSON] et al. [14] thought of merging features from a former layer in the residual signal branch based on the original ResNet for getting more features from the image under test. MobileNetV2, DenseNet121, and InceptionResNetV2 are used in [17] for testing an optimization strategy which uses a genetic algorithm to find the transfer learning and fine-tuning hyperparameters. Long training time, premature convergence, and slow convergent speed are some of the drawbacks encountered during the performance evaluation. To reduce the computational cost and to improve the performance, DenseNet is adopted in [18], which allows the computation of feature maps by linking the output from previous layers as inputs to the following layers. An ensemble model is conceived in [19] which consists of three pre-trained Convolutional Networks. The output of each of these networks is concatenated for producing a voting classifier ensemble architecture that makes insect classification possible, while in [19], the prediction of the class to which the pest under test belongs is made by hard voting. The authors in [20] adopt a soft voting module for obtaining pest classification. In particular, feature maps are aggregated to produce the activation map for accurate pest detection. Erroneous and unnecessary features are eliminated through the adaptive filtering fusion model by making use of the attention mechanism. Furthermore, an attention mechanism is used in [21] for enhancing the performance of the method, characterized by an architecture based on a spatial transformer network and ResNet. The authors in [22] have suggested a self-supervised transformer pre-training method for the development of a pest recognition method with better feature representation. A latent semantic mask auto-encoder is proposed, which makes the model capable of learning the semantic features of the pests under test. As the visual transformer architecture is unable to capture the appropriate discriminative information for fine-grained visual classification, the authors in [23] proposed an attention aggregating transformer with an information entropy selector capable of capturing differences between images. Moreover, in [24; 25], the extraction of fine grained features is performed by adopting multi-branch and multi-scale learning networks. A pest recognition based on a single-stage detection-object method is presented in [26; 27; 28] In more detail, the authors in [26] modify the YOLOv4 backbone network for allowing the extraction of deeper features and improving the recognition rate of densely distributed objects.