# Image Classification of Pests with Residual Neural Network Based on Transfer Learning
## 1 Introduction  
Pests are regarded as one of the leading causes of crop loss in the world [1]. Some crops such as rice and wheat are prone to being destroyed by pests, resulting in unstable yield and poor quality as well as huge economic losses to farmers. Therefore, how to protect crops from pests has become a necessary research area to maintain and even improve yield and quality. It is also vital for food security and agricultural economic development [2]. How to classify and identify pests is fundamental for pest monitoring and forecasting so as to prevent pests from further damaging crops [3]. Therefore, it is necessary to find an efficient, fast and automatic method of achieving pest classification. The traditional manual approach is very time-consuming and labor-intensive since the pest morphology is so similar that professional knowledge is needed [4]. Furthermore, the existing technicians capable of classification cannot meet the needs of gradually expandingdemands in terms of capability and quantity. Pest classification based on machine learning needs careful selection of features for different species. However, due to the numerous types of pests, the features of metamorphic development and the complicated crop environments, it is very difficult to identify the characteristics through artificial selection and meet the modern needs of applications [5]. With the great success of deep learning in multiple image processing fields and the rapidly increasing popularity of high-quality image capture devices [6; 7; 8; 9; 10], an automated pest identification system based on deep learning image processing is expected to be the best solution to reduce labor costs and improve classification performance [11; 12; 13; 14; 15]. Convolutional neural networks (CNNs) have also achieved breakthroughs in many fields, so the CNN-based image classification model is also considered significant. Research has recently been conducted in the field of pest classification. The CNN ensemble model proposed by [PERSON] et al. including a fine-grained model, attention mechanism and feature pyramid design has achieved accuracy as high as 74.13% and 99.78% in the IP102 dataset [16] and D0 dataset [17]. The integrated model SMPEnsemble [18] proposed by [PERSON] et al. based on the training of seven common CNN models has achieved accuracy of 67.13% and 95.15% in the IP102 dataset [16] and D0 dataset [17]. [PERSON] et al. adopted an ensemble model [19] of six CNNs and carried out the optimization by improving the DGrad and Adam algorithms [20], which achieved accuracy of 95.52%, 75.11% and 99.81% in the Deng dataset [21], IP102 dataset [16] and D0 dataset [17], respectively. The specific accuracy of each model is shown in Table 1.  
These results show that the scheme of automatic feature extraction based on a CNN is more effective than the traditional machine learning of manually selecting features in the field of agricultural pest classification. As shown in Table 1, the current research on pest classification based on the CNN model mostly adopts the scheme of ensemble model, and there is no clear solution to the problem of small database size and inability to fully meet the training requirements of the model. In addition, there is a wide range of biological similarities between pests. It is necessary to adopt an effective method to help the model fully learn the deep features of the samples. In this paper, we propose a convolution neural network pest classification method based on transfer learning and data enhancement. Based on the complexity of the exper