Deep learning models are widely used in the field of crop detection and recognition. Their advantage lies in optimizing the model by constructing different functional perception layers, which can automatically extract features from input data and achieve end-to-end learning. However, the unknown data processing process in this model leads to a lack of interpretability, which becomes the main obstacle to the application of deep learning. To overcome the shortcomings of insufficient interpretability in deep learning models, researchers have proposed an interpretability method based on class activation mapping. This article summarizes the research progress of the class activation mapping algorithm Grad-CAM in crop disease classification and detection, crop pest detection and recognition, crop variety classification, target crop detection, and other applications. It explains the advantages of the class activation mapping algorithm in visualizing convolutional neural networks with arbitrary structures, and analyzes the shortcomings of class activation mapping algorithms such as low interpretation precision, unstable gradients, lack of evaluation standards, and single application background. It proposes the development trend of building models with high accuracy and interpretability, construction of new interpretive algorithms, establishing unified evaluation standards for interpretable algorithms, and ensuring the correctness of interpretable algorithms.