CNNs often face limitations in capturing long-range dependencies and contextual relationships within images, particularly in complex and cluttered agricultural scenes. Recent developments in transformer-based architectures, such as vision transformers (ViTs), have demonstrated superior performance in visual tasks by modeling global feature relationships across image patches [4; 5].  
Compared to conventional CNN architectures, such as ResNet or EfficientNet, which rely on local receptive fields, vision transformers (ViTs) offer the advantage of modeling long-range spatial dependencies via self-attention mechanisms. This capability enables ViTs to better capture subtle and globally distributed features, which is particularly beneficial in insect pest classification, where fine-grained distinctions between species are required.  
Using these techniques and developing systems for automatic identification and continuous monitoring can allow rapid and targeted interventions, especially for the effectiveness of treatments and the minimization of pesticide application, as well as the strengths of ecological and sustainable agriculture that ensure the quality of cultivated products [6]. Due to the high performance on identification, speed, and accurate detection, systems integrating CNNs can reduce the risk of mass infestations. Ultimately, improved yield can be seen due to these integrations, and modern agricultural areas can have a significant ecological footprint [7].  
CNN architecture ensembles represent new trends in digital image processing and analysis for classification, segmentation, or object detection operations. Over the years, various studies in this field have been concerned with increasing the performance and robustness of these DL models [8; 9; 10]. ViT demonstrates remarkable, competitive performance, outperforming state-of-the-art CNN models. This is complemented by ViT's performance when trained on massive, extensive datasets.  
The main contributions of this study are as follows:  
1. We develop a novel ensemble system composed exclusively of five vision transformer (ViT) architectures--ViT Base, DeiT, MaxViT, FasterViT, and XCIT--which are independently trained and evaluated on benchmark insect datasets.
2. We propose a new logit-level fusion strategy in which class-wise F1 scores are used to compute a weight matrix that determines the contribution of each base model to each class prediction.
3. We employ logistic regression as a meta-classifier trained on scaled and weighted logits, offering a more expressive and fine-grained fusion compared to classical SoftMax averaging or majority voting methods.  
These contributions provide an effective and scalable solution for insect pest classification in real-world agricultural settings, offering both accuracy and robustness. Considering these characteristics, the present paper applies ViT models to insect pest detection, highlighting their flexibility and potential to create innovative solutions for the varied contexts of modern agriculture [11]. This work presents modern ViT architectures to combine them as an ensemble-type decision system to increase performance in the classification task. The proposed system, built by a new fusion method, considers decision-making based on the associated weights for each component network, and each predicted class outperforms the lower-level components represented by the individual models.  
Apart from the introduction, the paper consists of the following sections. Section 2 presents related works in the addressed field, following a series of challenges and limitations for the present task. Section 3 contains the materials and methods of the study, with emphasis on datasets, CNN architecture used, hardware and software areas, and implementation of the proposed system. Section 4 notes the experimental results. Some discussions, highlighting the strengths and analyzing the proposed methodology from various perspectives, are presented in Section 5. Finally, Section 6 concludes the paper.