# Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
## 1 Introduction
These advancements have significantly improved not only detection accuracy but also the efficiency of object detection in images and videos. Furthermore, this technological progress has unlocked new potential for enhancing the detection accuracy of small-sized objects. The main contributions of this paper are as follows:  
1. Improved detection method: An improved Yolov-tiny-based target detection method is proposed, which enhances the detection accuracy for farmland pests by limiting downsampling and incorporating the Convolution Block Attention Module (CBAM). 2. Experimental validation: Extensive experiments are conducted using images of pests common to seven types of farmland, demonstrating that the improved model increases the average precision by 7.18% compared to the original Yolov-tiny model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Algorithm** & **Features** & **Results** \\ \hline \multirow{3}{*}{[PERSON] et al. [8]} & \multirow{3}{*}{R-FCN + ResNet-50} & Compared combinations of & \multirow{3}{*}{Detected nine typical types of tomato pests, mAP = 0.8598} \\  & & seven feature extractors with a meta-framework & & \\ \hline \multirow{3}{*}{[PERSON] et al. [9]} & Improved Faster & \multirow{3}{*}{Employed federated learning, enhanced detection speed for small targets} & Multi-pest detection, \\  & R-CNN + ResNet-101 & & small detection speed for small targets & \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [10]} & \multirow{3}{*}{Faster R-CNN + ResNet101} & Explored various & \multirow{3}{*}{mAP = 74.77\%} \\  & & deep-learning models for classifying crop pests & \\ \hline \multirow{3}{*}{[PERSON] et al. [11]} & Improved Faster & \multirow{3}{*}{Substituted VGG16 with ResNet50, resNet50, incorporated soft non-maximum suppression} & Improved detection accuracy \\  & R-CNN + ResNet50 & & ResNet50, incorporated soft non-maximum suppression & \\ \hline \multirow{3}{*}{[PERSON] et al. [13]} & Deformable Residual & Global context-aware, & Improved pest \\  & Network Module & enhanced feature extraction & recognition accuracy \\ \hline Di and Li [14] & Improved Convolutional & \multirow{3}{*}{Detected apple leaf diseases} & Significantly increased detection accuracy \\ \cline{1-1}  & Neural Network & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the Existing Literature. 3. Optimization techniques: The paper introduces novel optimization techniques, including adaptive image equalization, image denoising, and mosaic image augmentation, to improve the clarity and detail of pest images, further enhancing the model's accuracy. 4. Comprehensive dataset: A specialized pest image dataset is curated and preprocessed, providing a robust foundation for training and validating the improved detection model. 5. Practical applications: The research highlights the practical implications of the improved detection model for real-time pest monitoring and management in agricultural fields, contributing to the mitigation of pest-related losses in agriculture. The organization of this paper is as follows:  
1. Section 2 outlines the model structure of the Yolox-tiny object detection algorithm;
2. Section 3 details enhancements to the Yolox-tiny model to address challenges in detecting small objects;
3. Section 4 describes the preprocessing of image data within the dataset to enhance detection accuracy and details the process of feeding this preprocessed data into the model for training. Additionally, it covers comparative experiments conducted using the same dataset to verify the accuracy of the enhanced model;
4. Section 5 presents the conclusions derived from the experimental comparisons.