# GE-YOLO for Weed Detection in Rice Paddy Fields
## 1 Introduction  
Asia is the largest rice-producing and consuming region in the world, with China, India, Indonesia, Bangladesh, Vietnam, and Thailand being the major rice-producing countries. Under the pressure of a burgeoning population and diminishing farmland [1]. As one of the world's major food crops, rice production needs to shift towards more efficient, intensive, sustainable, and automated production models. Weeds are competitive factors in the growth of crops, including rice, as they occupy soil nutrients, water, and sunlight resources, adversely affecting crop growth and yield. By effectively detecting and removing weeds, the growth environment of crops can be improved, yield can be increased, and agricultural production efficiency can be enhanced. Traditional weed detection and removal methods often require significant human labor, are costly and inefficient, and the excessive use of chemical herbicides can lead to soil and water pollution. Utilizing automated weed detection technology can reduce manual intervention, lower operating costs and herbicide overuse, improve the economic efficiency and safety of agricultural production, and assist in moving rice production towards intelligent, intensive, and efficient production methods. To date, research on weed detection can be broadly categorized into two main approaches: traditional computer-vision-based methods and deep-learning-based methods. Traditional methods typically use image processing, feature extraction, and classification techniques from traditional computer vision to detect weeds. [PERSON] and [PERSON] [2] proposed two classification methods for weed density classification: a support-vector-machine-based method and a method based on scale-invariant feature transform and random forest. The former achieved an accuracy of 73%, while the latter reached 86%, indicating limitations in accuracy. [PERSON] et al. [3]used a series of non-deep-learning methods to extract features and classify images of sugar beet and thistles in the \"thistle detection in sugar beet fields\" problem, achieving an accuracy of over 90%. However,these methods have high computational complexity, slow image processing speed, and poor adaptability to different environmental conditions in practical applications. Overall, traditional computer-vision-based weed detection algorithms are generally sensitive to factors such as image quality, lighting conditions, and image backgrounds, resulting in unstable and less robust detection results and thus lower practicality. With the development of deep learning technology in recent years, deep-learning-based weed detection techniques have been increasingly applied. These methods typically use convolutional neural networks (CNNs) or their variants, as well as encoder-decoder structures for feature extraction and classification, enabling end-to-end training and detection. They are gradually being widely used for weed detection, plant disease, pest detection, and segmentation tasks, achieving high accuracy and image processing speed. [PERSON], [PERSON], et al. [4] used a cascade model to train multiple small networks similar to U-Net for image segmentation of rice field weeds. [PERSON], [PERSON], and [PERSON] proposed a fully automatic annotation and training deep network to address weed recognition in UAV remote sensing images [5]. [PERSON] and [PERSON] proposed a lightweight network Lite-MDC combining MdsConv to solve the disease area recognition problem of pigeon pea [6]. [PERSON] et al. proposed WeedDet, a model based on RetinaNet, to address weed recognition in rice fields with high overlap between weeds and crops, achieving an mAP of 94.1% and an FPS of 24.3 [7]. [PERSON] et al. used multiple deep learning architectures, including ResNet, VGG16, VGG19, AlexNet, GoogleNet, and LeNet, to train a weed detection model for soybean crops [8]. [PERSON], [PERSON] et al. used AlexNet to segment weeds in cabbage images captured by drones [9]. [PERSON] et al.