Researchers are exploring various deep learning approaches for image classification and object detection in the context of pest and disease identification in crops. Different architectures and techniques are being proposed, including adaptations of existing models such as DenseNet and ResNet, as well as new methods incorporating attention mechanisms and sensor fusion. The goal of these efforts appears to be improving the accuracy and efficiency of pest and disease detection in agricultural settings.