Abstract: Traditional methods for classifying crop pests are based on the appearance of the pests such as their color, morphology and texture using algorithms s back propagation neural network and support vector machine. These methods are sensitive to the environments where the pests appear. Also, the imbalance between training sample numbers, difference in scales at which the training samples are taken, along with that the pests might seasonally change their color and shape, make it difficult for these algorithms to detect and recognize the pests. In order to improve the accuracy of detecting crop pests appearing in diverse environments, this paper proposed a deep convolutional neural network method by combining the spatial pyramid pooling with the improved YOLOv3 deep convolutional neural network algorithm. The proposed method first located the pests on a test image and then identified the species they belongs to. Using the improved network structure in YOLOv3, the proposed method used up-sample and convolution operations to calculate the de-convolution. Bilinear interpolation was used to enhance the output of the network, and the depth characteristics of the pests were extracted by the depth residual neural network. As a result, the improved network can effectively detect and recognize small pests in the images. By fusing with the spatial pyramid pooling, the Yolov3-SPP network can map the extracted eigenvectors to different spatial dimensions. The network can be used at various scales to detect pests of different sizes. Results from identifying and testing 20 types of pests collected by traps showed that the average accuracy of the proposed method was 88.07%, with a detection speed of 26 frames/s. Compared with the typical YOLOv3 algorithm, the proposed methods improved accuracy by 2.8 percentage points. We also reconstructed the maps of the network at each stage and compared them with the typical YOLOv3 algorithm in attempts to demonstrate that the features extracted by the proposed method was more recognizable. The results revealed that the typical YOLOv3 algorithm could miss the targeted objects, while the proposed method is not only able to detect pests of different sizes, it also has high recognition accuracy. Using the same dataset, we compared the proposed method with other detection algorithms such as HOG+SVM, Faster R-CNN, YOLOv3. The comparison showed that the proposed method was 19.61 percentage points higher than that of HOG + SVM, and 9.64 percentage points higher than that of Faster R-CNN, Faster R-CNN was unable to extract small pests, and its recognition accuracy was only 78.43%.