# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 1 Introduction  
The traditional crop pest monitoring system relies on observation and analysis by the administrator [1]. Incorrect judgments occur frequently. With the rise of machine learning technology, detecting pests using machine learning techniques has become common [2]. [PERSON] et al. [3] developed an automated pest monitoring system. The system uses a drone to capture images and extracts the histogram of oriented gradient (HOG) features of grayscaled images to feed into a support vector machine (SVM) to identify pests. The system's detection precision of 88.9% on a self-built dataset exceeds that of methods using other feature descriptors, indicating that HOG can extract high-quality features. This process of first extracting features from images and then inputting them into a machine learning classifier is used by many pest monitoring systems [4]. Following this standard process, [PERSON] et al. [5] designed a pest detection system for sticky traps. The system uses k-means to segment the pest images captured by sticky traps and converts the color space to HSI, which is more suitable for use in the detection task to extract features such as the mean and standard deviation. In the classification stage, the authors evaluated the performance of the K-nearest neighbor (KNN) and the decision tree. The KNN outperformed the decision tree in several metrics such as precision and recall, largely because KNN can classify pests with reference to past knowledge. In addition, the application of color space and image segmentation in the system provides new tools for building pest detection systems. Additionally, using these tools, [PERSON] et al. [6] used image processing methods suchas color space transformation and threshold segmentation from the HALCON library to segment the original pest images and extract the features that are used for classifying pests in a machine learning classifier. It is worth mentioning that this pest detection method uses a Multilayer Perceptron (MLP) that performs better than common machine learning classifiers such as KNN in the classification stage, and achieves a far superior precision in the ten-fold cross-validation. This is mainly due to the deeper structure of the MLP, which can learn more complex mapping relationships. Pest detection methods based on machine learning techniques rely on the artificial selection and extraction of visual features, which limits the generalization ability of feature representation, resulting in such methods being susceptible to interference from background changes, morphological changes, and other factors, as well as the instability of detection precision [7]. The rise of convolutional neural networks has greatly improved the generalization of feature representations and improved the performance of learning systems in various tasks. [PERSON] et al. [8] used cameras fixed in farmland to take pictures of the pests and diseases, and enhanced the quality of the original data by image processing methods such as color space conversion and threshold segmentation. These images were finally fed into Single Shot MultiBox Detector (SSD), Faster R-CNN, and Mask R-CNN to detect pests. Thanks to the more complex network structure, Faster R-CNN achieved the best results on most classes. [PERSON] et al. [9] evaluated various convolutional neural networks such as ResNet and Visual Geometry Group (VGG), and built a garbage classification system with ResNet-34, as well as introducing multiplex feature fusion, a feature reuse mechanism and new activation functions to improve the classification performance. The results of the ablation experiment show the effectiveness of the improvements. The final precision achieved was 99.9%, which fully demonstrated the powerful performance of convolutional neural networks. [PERSON] et al. [10] pioneered the application of YOLOv3, an object detection method based on convolutional neural networks, in the field of gesture recognition and trained a YOLOv3-based gesture recognition model using a gesture dataset constructed from videos.