# A New Hybrid ConvViT Model for Dangerous Farm Insect Detection
## 1 Introduction
With these developments in artificial intelligence technologies, many applications have emerged in the agricultural scope--for example, automatic detection, surveillance, and identification of insects [2]. Modern agriculture constantly evolves, and technology is critical in growing crops and protecting them from pests. The adaptation of artificial intelligence has quickly become the subject of interdisciplinary studies and has become widespread in various fields; agriculture is vital. Thanks to technological developments in artificial intelligence, important outputs have been produced in many fields. These outputs have made such technologies widespread in interdisciplinary studies, especially in agriculture; with the integration of artificial intelligence, detecting harmful insects is seen as an important output. When the field of agriculture is considered, it is seen that artificial intelligence techniques are increasing in popularity and providing benefits on a large scale. The most important are the effective use of human power, the ability to do work quickly, and the speed of processing data. These features, especially recently, go beyond many processes; for example, early diagnosis, surveillance, and classification are the most important outputs. As long as deep learning, a sub-branch of artificial intelligence, continues, it plays a critical role in detecting harmful insects. Thanks to these developments, harmful insects can be detected quickly and effectively [6]. Smart agriculture has become an important field in recent years due to its use of artificial intelligence techniques, information, and wireless communication technologies, especially in many areas of agriculture, such as the control of crop diseases, fertilization, detection of plant diseases, and detection of harmful insects. However, detecting and classifying harmful insects is difficult for farmers because their structures are complex and highly similar. In addition, traditional manual detection systems are ineffective, costly, and time-consuming. However, early detection of plant damage plays an important role for farmers. Therefore, computer vision systems and image-processing techniques that use artificial intelligence are widely used in many studies and are still used today. Image-based pest detection systems can be used for many purposes. For example, in the biodiversity index, pest counting is used in agriculture. Evidence-learning methods such as convolutional neural networks are a good solution for automatic pest detection. Compared to classical image-processing techniques and machine learning techniques, convolutional neural networks process raw pixels and, at the same time, extract new features. In addition, CNN provides robustness against noise in the image and illumination against diversity. It is used in medical image processing [7]. This paper introduces a hybrid ConvViT model that integrates the local feature extraction strengths of convolutional neural networks with the global contextual analysis of vision transformers to improve pest detection in agriculture. Unlike traditional methods that struggle in complex environments or with visually similar species, this approach leverages both architectures for more reliable identification of harmful pests. By ensuring balanced generalization and robust performance, the ConvViT model supports sustainable agriculture through early and accurate detection, reducing excessive pesticide use and enhancing overall crop management. The primary goal of this paper is to provide a reliable automated detection system that aids in pest management strategies, ultimately contributing to more precise and targeted pest control measures. The proposed model is a means to achieve accurate pest control, rather than an end goal in itself. The rest of this paper is organized as follows: Section 2 outlines the studies focused on deep learning-based harmful insect detection, providing an overview of existing methodologies and their limitations.