This review discusses the application of deep learning in weed-crop recognition for smart agricultural equipment. Weeds and crops compete for resources, reducing crop yields and increasing agricultural costs. Traditional weed control methods, such as chemical herbicides, have drawbacks like promoting weed resistance and environmental pollution. Smart agricultural equipment, including robots, drones, and satellites, offers a solution by enabling accurate detection and targeted actions like minimal pesticide spraying or precise laser excision. Deep learning enhances weed recognition accuracy, leading to efficient and eco-friendly weed management. The review covers the use of various sensors, such as RGB, multispectral, hyperspectral, and LiDAR, for data collection. Image preprocessing techniques, including resizing, enhancement, denoising, and background removal, are essential for effective recognition. Feature extraction involves visual textures, spatial contexts, spectral features, and biological morphological features. Deep learning models, such as CNNs, Faster R-CNN, YOLO, and Transformers, are used for weed and crop identification. These models achieve high accuracy, with some reaching over 96% classification accuracy. Recent applications include spot photographic image recognition, where deep learning models classify weed density and improve pesticide use efficiency. Satellite image recognition uses high-resolution data for large-scale monitoring, though challenges like cloud cover and long revisit intervals limit its effectiveness. The review highlights the importance of hybrid feature extraction and the role of attention mechanisms in improving model performance. Overall, deep learning-based weed-crop recognition is a promising approach for sustainable and efficient agriculture.This review discusses the application of deep learning in weed-crop recognition for smart agricultural equipment. Weeds and crops compete for resources, reducing crop yields and increasing agricultural costs. Traditional weed control methods, such as chemical herbicides, have drawbacks like promoting weed resistance and environmental pollution. Smart agricultural equipment, including robots, drones, and satellites, offers a solution by enabling accurate detection and targeted actions like minimal pesticide spraying or precise laser excision. Deep learning enhances weed recognition accuracy, leading to efficient and eco-friendly weed management. The review covers the use of various sensors, such as RGB, multispectral, hyperspectral, and LiDAR, for data collection. Image preprocessing techniques, including resizing, enhancement, denoising, and background removal, are essential for effective recognition. Feature extraction involves visual textures, spatial contexts, spectral features, and biological morphological features. Deep learning models, such as CNNs, Faster R-CNN, YOLO, and Transformers, are used for weed and crop identification. These models achieve high accuracy, with some reaching over 96% classification accuracy. Recent applications include spot photographic image recognition, where deep learning models classify weed density and improve pesticide use efficiency. Satellite image recognition uses high-resolution data for large-scale monitoring, though challenges like cloud cover and long revisit intervals limit its effectiveness. The review highlights the importance of hybrid feature extraction and the role of attention mechanisms in improving model performance. Overall, deep learning-based weed-crop recognition is a promising approach for sustainable and efficient agriculture.