# Weed Detection Method Based on Lightweight and Contextual Information Fusion
## 1 Introduction  
Sugar beet, as a crop, plays a crucial role in global food and economic systems. However, during the growth of sugar beet, various types of weeds in the field compete for water, nutrients, and sunlight, significantly impacting crop yield and quality [1]. Research indicates that weed growth can lead to crop yield losses of up to 34% [2]. Weed removal and control have thus become integral components of modern agricultural production. Existing weed control methods primarily include mechanical weeding, traditional pesticide application, and robotic weeding, among others. Mechanical weeding, though effective, comes with substantial labor costs and the potential for crop damage [3]. Over-reliance on pesticides for weed control can result in environmental pollution and pesticide residues [4]. With the proliferation of artificial intelligence technologies, modernized weed control methods such as laser weeding robots and pesticide spraying robots have emerged. These robots are capable of precise identification and localization of crops and weeds during their operations [5, 6]. However, achieving real-time detection and recognition of crops and weeds remains a critical challenge. Currently, optical technologies encompass optical detection techniques, digital image processing, curvelet transform detection, and computer vision detection techniques, such as hyperspectral imaging technology and terahertz spectroscopy imaging, which have foundwidespread applications and development in object detection [7; 8; 9; 10]. Optical technologies have been employed extensively, including the use of digital image processing for fruit grading and sorting [11], and curvelet transform detection for identifying plants infected with diseases [12]. However, these methods are associated with high detection costs or limited applicability. Consequently, computer vision technology is primarily considered. Table 1 outlines the advantages and disadvantages of these current detection methods. In recent years, with the advancement and application of computer vision, agriculture has experienced significant progress in terms of intelligence [13; 14; 15]. Deep learning, as a subset of computer vision, is known for its ability to rapidly detect, locate, and recognize targets. Consequently, deep learning is now widely applied in agricultural weed control [16]. In the field of target recognition, algorithms are broadly categorized into one-stage and two-stage methods [17]. Faster R-CNN [18], a representative two-stage algorithm, has gained widespread adoption. [PERSON] et al. [19] successfully detected weeds in complex field backgrounds by integrating the ResNeXt network with the FPN for feature extraction. However, Faster R-CNN, being a two-stage algorithm, has the drawback of relatively slow inference speed. Two-stage algorithms require the initial filtering of candidate boxes, followed by determining whether the candidate boxes enclose the target for detection. Subsequently, adjustments to the position of the target are made, leading to a slower process. On the other hand, one-stage algorithms do not involve the candidate box filtering step. Instead, they directly regress the position coordinates of the target box and the classification probability of the target, resulting in a faster operation. On the other hand, one-stage algorithms, which perform object classification and localization in a single step, are exemplified by YOLO (You Only Look Once). YOLO is known for its high-speed detection and accuracy [20]. [PERSON] et al. [21] proposed YOLOv4-weeds, an improved algorithm based on YOLOv4 [22]. YOLOv4-weeds replaced YOLOv4's backbone network with MobileNetV3-Small and introduced depthwise separable convolutions and attention mechanisms, making the detection model more efficient. [PERSON] et al. [23] presented YOLO-CBAM, a convolutional neural network model that combines YOLOv5 and attention mechanisms for weed detection. It achieved an average precision improvement of 2.49% and real-time detection on Jetson AGX Xavier. [PERSON] et al.