# Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
## 1 Introduction  
China, a major agricultural nation, is experiencing an intensifying impact of pests on crops such as early rice, rice, and corn. Each year, diseases and pests result in a loss of 14 million tons of grain, corresponding to direct economic losses of at least 2 billion RMB. Consequently, researching pest monitoring techniques in farmlands is crucial to ensuring agricultural production [1; 2; 3]. Existing agricultural pest monitoring technologies can be categorized into manual and intelligent methods. Manual pest monitoring, known for its labor-intensive and time-consuming nature, limits the timeliness and effectiveness of pest control management. In recent years, significant advancements have been made in intelligent pest monitoring technologies. Researchers such as [PERSON] et al. and [PERSON] et al. significantly improved identification and classification accuracy, marking a new era in precision agriculture. As a result, intelligent pest monitoring technologies have become increasingly prioritized in recent agricultural research. These advancements highlight the shift towards more intelligent pest monitoring methods [4; 5; 6; 7]. Over the years, deep learning algorithms have been developed and implemented, achieving notable success in the recognition and detection of agricultural pests. For example, [PERSON] et al. established an R-FCN with a ResNet-50 deep learning model by comparing combinations of seven feature extractors with a meta-framework. This model reached a mean Average Precision (mAP) of 0.8598 in detecting nine typical types of tomato pests [8]. [PERSON] et al. introduced a multi-pest detection technique employing federated learning with an improved Faster R-CNN that uses ResNet-101 instead of VGG-16 for the base convolutional layer. This approach notably enhanced the detection speed for small targets, achieving an mAP of 90.27% in multi-pest detection [9]. [PERSON] and [PERSON] explored various deep-learning models for classifying crop pests. Among these, the Faster R-CNN with a ResNet101 feature extractor emerged as the most effective, achievingan mAP of 74.77% [10]. [PERSON] et al. enhanced Faster R-CNN by substituting VGG16 with ResNet50 for feature extraction and incorporating soft non-maximum suppression to improve recognition of overlapping objects. This modification boosted the detection accuracy for small objects to 83.26%, underscoring the effectiveness of deep learning in agricultural object detection [11]. [PERSON] and [PERSON] provided a comprehensive survey on various deep learning and machine learning methods used for crop pest detection, evaluating their advantages and limitations [12]. [PERSON] et al. proposed a global context-aware-based deformable residual network module for precise pest recognition and detection, demonstrating improved recognition accuracy through enhanced feature extraction [13]. Additionally, [PERSON] and [PERSON] developed a method for detecting apple leaf diseases based on an improved convolutional neural network, which significantly increased detection accuracy [14]. However, these models exhibit low detection accuracy for small targets. Addressing this limitation and enhancing detection accuracy for small target pests represent urgent issues and are the primary focus of this paper [15]. To provide a clear comparison, Table 1 summarizes the algorithms, key features, and results highlighted in the existing literature. Object detection has emerged as a crucial task across various domains of computer vision. Introduced in 2015, the Yolo algorithm model has seen widespread application in this field [16; 17; 18; 19; 20]. The Yolo algorithm has evolved into a series, comprising Yolov1, Yolov2, Yolov3, Yolov4, Yolov5, and Yolov, among others [21; 22; 23; 24; 25; 26]. Beginning with the introduction of anchor-based detection in Yolov1, Yolov employs anchor-free detection by directly extracting and regressing features from images or videos.