## 2 Related Works  
Recent papers note CNNs' success in identifying pest insects in agriculture. Researchers have developed and implemented various architectures to distinguish between different insect pests affecting crops, based on intelligent systems and data from real scenarios. Digital images illustrating scenarios taken from the field are input data for training DL architectures [12,13]. The larger and better oriented the dataset is (providing a higher quality of input data), the higher the performance obtained, and the more accurately it can identify the pests in the targeted areas. The authors of [1] focused on crop pest identification using CNNs and a custom digital image dataset from real agricultural environments. The proposed architecture is enhanced by a parallel attention mechanism to address the challenges of effective detection in real scenarios. A proposed model, ResNet50-PCSA, integrates a channel and spatial attention module to correctly extract relevant features of insects, highlighting them against a complex background. The experiments showed that the model proposed by the authors achieved a remarkable accuracy of over 98%, being trained and evaluated using a dataset that included images taken from agricultural areas. To automatically monitor important insect pest populations, the authors of [14] implemented a YOLOv5-based architecture to identify two fly species affecting Mediterranean crops. An overall accuracy of 93% underlined the significant advantages of DL models over traditional methods, which are often expensive and subject to various human errors. At the same time, the researchers noted a potential extension of the proposed system's applicability to other insect species, providing a scalable solution for modern IPM methodologies. A recent study [15] showed that classification architectures based on residual networks achieved remarkable results in the identification process of insect pests. However, complications occurred because of the pests' complicated behavior and appearance changes throughout their lifecycle. To address these issues, the author proposed a multi-task learning system based on a discriminative attention multi-network (DAM-Net) module, whose job is to extract key features from digital images to help and improve the identification process of insects. For enriching texture details and global contextual information, a ResNet50 architecture was used. It was observed that these techniques were useful in understanding the data from the images that make up the datasets. In this case, the method demonstrated remarkable results with an accuracy of 99.7% on the D0 dataset and 74.1% on the IP102 dataset. The proposed DL techniques can create architectures to improve the system's performance and robustness in different agricultural contexts. The authors of [16] proposed an innovative method called k-nearest neighbors distance entropy (KNN-DE) as an effective method to evaluate informative data, applied to agricultural pests' identification. This research noted the importance of data quality instead of data quantity for accurate pest recognition. The authors noted that using high-quality data and learning based on few-shot learning can greatly reduce the resources needed to train CNN models, and such an approach can have a major impact on precision agriculture practices by applying smart and sustainable technologies. Pursuing the maximization of performance metrics for these applications, new trends are represented by the CNN combination models. Major advantages of such an approach are increasing accuracy and improving robustness by implementing a global system, based on the decision of several architectures [17,18]. By using an ensemble of models, one can explore the strengths of each model while also noting specific vulnerabilities [19]. In this case, one model may be more effective at detecting insect-specific features, and another model may excel at identifying insects depicted in complex scenarios, under different lighting conditions, and in various poses.