Abstract: With the widespread application of artificial intelligence in mining production,it has become an inevitable trend to improve the accuracy and efficiency of ore grade prediction using computer vision technology. This article reviews the application and development process of traditional image processing algorithms and deep learning algorithms in ore grade prediction, and discusses future trends and challenges. Traditional image processing techniques extract features such as size,color, texture,and flow rate of foam images,and combine algorithms such as watershed segmentation,color moments,gray-level co-occurrence matrix,and local feature matching for feature extraction. These features have certain application value in scenarios with limited computing resources but have lower accuracy in tackling ore grade prediction tasks. Deep learning techniques,on the other hand,can extract high-level semantic features by constructing suitable model architectures and training them with large amounts of data. They have higher predictive accuracy and can achieve high performance and efficiency when used in conjunction with efficient computing devices such as Graphics Processing Units(GPUs). This article also introduces machine learning algorithms such as Support Vector Machines(SVM),Extreme Learning Machines(ELM),as well as deep learning algorithms such as Multilayer Perceptron(MLP),fully connected layers,and multi-scale feature fusion for feature mapping and grade prediction. The development history of deep learning models is also discussed. Finally,the current application status of industrial visual inspection systems is reviewed,and the challenges and future development trends in this field are analyzed from aspects such as data-driven models,multimodal data fusion,algorithm real-time performance,and dataset scale.