The paper "Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review" by Hao-Ran Qu and Wen-Hao Su provides an in-depth overview of the application of deep learning in smart agricultural equipment for weed and crop recognition. The authors highlight the challenges posed by weeds in agriculture, such as reduced crop yields and increased costs, and the need for innovative solutions to promote sustainable and eco-friendly practices. They discuss the limitations of traditional weed control methods, including heavy herbicide use, which can lead to weed resistance and environmental pollution. The paper emphasizes the role of smart agricultural equipment, such as intelligent robots, unmanned aerial vehicles (UAVs), and satellite technology, in addressing these challenges. The effectiveness of these smart agricultural tools depends on accurate detection, which is influenced by factors like growth stages, environmental conditions, and shading. The authors review the use of suitable sensors and optimized algorithms, with a focus on deep learning, to enhance weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, reducing overall agricultural costs. The paper covers the development of intelligent agricultural tools, sensors, and identification algorithms, and provides instructive examples of their prowess in distinguishing between weeds and crops. It highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing future directions. By integrating cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture. The review also delves into the workflow of image recognition, including image data collection, preprocessing, feature extraction, and classification. It discusses the use of various sensors, such as RGB, multispectral, hyperspectral, and LiDAR sensors, for data acquisition. The paper further explores image processing techniques, data augmentation, and feature extraction methods, including visual texture, spatial context, spectral, and biological morphological features. Finally, the paper reviews the applications of deep learning algorithms in weed/crop discrimination, focusing on deep neural networks (DNNs) and their variants, such as convolutional neural networks (CNNs) and attention mechanisms. It provides a comprehensive overview of the latest advancements and their impact on smart agricultural equipment, emphasizing the potential for improved precision and efficiency in weed management.The paper "Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review" by Hao-Ran Qu and Wen-Hao Su provides an in-depth overview of the application of deep learning in smart agricultural equipment for weed and crop recognition. The authors highlight the challenges posed by weeds in agriculture, such as reduced crop yields and increased costs, and the need for innovative solutions to promote sustainable and eco-friendly practices. They discuss the limitations of traditional weed control methods, including heavy herbicide use, which can lead to weed resistance and environmental pollution. The paper emphasizes the role of smart agricultural equipment, such as intelligent robots, unmanned aerial vehicles (UAVs), and satellite technology, in addressing these challenges. The effectiveness of these smart agricultural tools depends on accurate detection, which is influenced by factors like growth stages, environmental conditions, and shading. The authors review the use of suitable sensors and optimized algorithms, with a focus on deep learning, to enhance weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, reducing overall agricultural costs. The paper covers the development of intelligent agricultural tools, sensors, and identification algorithms, and provides instructive examples of their prowess in distinguishing between weeds and crops. It highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing future directions. By integrating cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture. The review also delves into the workflow of image recognition, including image data collection, preprocessing, feature extraction, and classification. It discusses the use of various sensors, such as RGB, multispectral, hyperspectral, and LiDAR sensors, for data acquisition. The paper further explores image processing techniques, data augmentation, and feature extraction methods, including visual texture, spatial context, spectral, and biological morphological features. Finally, the paper reviews the applications of deep learning algorithms in weed/crop discrimination, focusing on deep neural networks (DNNs) and their variants, such as convolutional neural networks (CNNs) and attention mechanisms. It provides a comprehensive overview of the latest advancements and their impact on smart agricultural equipment, emphasizing the potential for improved precision and efficiency in weed management.