The document discusses a crop pests image classification algorithm utilizing deep convolutional neural networks (DCNN), primarily focusing on the effectiveness of Lenet-5 and AlexNet architectures. Experiments classified 82 common pest types with high accuracy, achieving up to 91%, and demonstrated that DCNNs outperform conventional classification methods. The study highlights the challenges of traditional classification techniques and illustrates how deep learning improves pest image classification accuracy by leveraging extensive datasets and sophisticated neural network structures.