# Deep Learning-Based Image Recognition of Agricultural Pests
###### Abstract  
Pests and diseases are an inevitable problem in agricultural production, causing substantial economic losses yearly. The application of convolutional neural networks to the intelligent recognition of crop pest images has become increasingly popular due to advances in deep learning methods and the rise of large-scale datasets. However, the diversity and complexity of pest samples, the size of sample images, and the number of examples all directly affect the performance of convolutional neural networks. Therefore, we designed a new target-detection framework based on Cascade RCNN (Regions with CNN features), aiming to solve the problems of large image size, many pest types, and small and unbalanced numbers of samples in pest sample datasets. Specifically, this study performed data enhancement on the original samples to solve the problem of a small and unbalanced number of examples in the dataset and developed a sliding window cropping method, which could increase the perceptual field to learn sample features more accurately and in more detail without changing the original image size. Secondly, combining the attention mechanism with the FPN (Feature Pyramid Networks) layer enabled the model to learn sample features that were more important for the current task from both channel and space aspects. Compared with the current popular target-detection frameworks, the average precision value of our model ([EMAIL_ADDRESS]) was 84.16%, the value of ([EMAIL_ADDRESS]:0.95) was 65.23%, the precision was 67.79%, and the F1 score was 82.34%. The experiments showed that our model solved the problem of convolutional neural networks being challenging to use because of the wide variety of pest types, the large size of sample images, and the difficulty of identifying tiny pests. p 2022  
Pests and diseases have always been a complex problem in agricultural production, as they cause large economic losses every year. To effectively prevent and control pests and diseases in farm fields, pest and disease information needs to be collected and analyzed [16; 17]. Owing to the diversity of pests on farmland and the complexity of available information types, the traditional pest-monitoring methods through manual observation and statistics can no longer meet the needs of modern large-scale agricultural production for pest control work [18; 19; 20]. Therefore, applying deep-learning methods to the intelligent recognition of crop pest images has become a critical research hotspot [16; 17; 18; 19; 20; 21; 22; 23; 24]. Researchers have recently combined deep learning to develop many object-detection frameworks for pest identification. [PERSON] et al. [16] achieved the localization and identification of 23 key pest species in tea plantations using salient maps and a CNN (Convolutional Neural Network). [PERSON] et al. [17] identified 35 insect species using a sparse coding pyramid model. [PERSON] et al. [18] used an attentional convolutional neural network to identify soybean pests. [PERSON] et al. [19] introduced an AlexNet-based residual block to construct a new pest-identification network to identify ten pests in the natural environment. [PERSON] et al. combined the excellent parts of previous models to detect nine diseases and insect pests on tomato plants [20] and achieved good results. [PERSON] et al. [21] developed a new target-detection model that uses fast R-CNN as the terminal model to classify 24 pest categories. [PERSON] et al. [22] proposed a new convolution and recursion hybrid network that combined AlexNet and bidirectional short-term memory (BiLSTM) to identify wheat grains damaged by pests. [PERSON] et al. [23] developed an interactive Android and web interface based on CNN to diagnose problems and diseases in crops. [PERSON] et al. [24] developed a new object-detection framework based on faster R-CNN, which could conduct online monitoring of plant diseases and pest identification.