# GE-YOLO for Weed Detection in Rice Paddy Fields
## 1 Introduction
conducted a comparative study on the effectiveness of five convolutional neural networks, including VGG16, ResNet50, DenseNet121, EfficientNetB0, and MobileNetV2, for detecting weeds in a field [10]. However, there are still challenges in accurately detecting weeds such as Alternanthera sessilis, Polygonum lapathifolium, and Beckmannia syzigachne in rice fields, as these weeds are hidden, have similar color and morphology to crops, are affected by water surface reflections, have complex distribution environments, and undergo growth changes over time, leading to low accuracy and robustness of visual detection algorithms. Further research is needed to address these issues. A prime example of deep-learning-based visual detection algorithms, You Only Look Once (YOLO) is a series of object detection algorithms that, compared to earlier two-stage algorithms like R-CNN [11], Fast-RCNN [12], and Faster-RCNN [13], achieves one-stage detection. This means that YOLO does not involve the process of generating region proposals but directly regresses the bounding boxes and class probabilities of objects within a single neural network, thus unifying the object detection problem into a single regression problem. Compared to the R-CNN series algorithms, YOLO offers faster inference speed and higher accuracy. To date, the YOLO series has developed multiple versions, including YOLOv3 [14], YOLOv4 [15], YOLOv5 [16], YOLOv6, YOLOv7 [17], YOLOv8, YOLOv9 [18],YOLOv10 [19], and the latest version, YOLOv11. Some methods such as YOLOv5 and YOLOv7 are widely used in various fields such as crop detection and recognition, fruit and vegetable quality detection, plant disease and pest detection, and weed recognition due to their lightweight, fast, and high recognition rate characteristics, promoting intensive and automated agricultural production. [PERSON] and [PERSON], for example, proposed an efficient model for distinguishing between local and foreign cotton based on YOLOv7 called \"Cotton-YOLO\" [20]. [PERSON] and [PERSON] combined YOLOvX, CBAM, and ASFF to recognize key growth stages of lettuce [21]. [PERSON], [PERSON], and [PERSON] developed a model based on YOLOv5s called \"GTCBS-YOLOv5s\" to identify six types of rice field weeds (including Eclipta prostrata, Euphorbiee semen, Cperyus difformis, [PERSON] arenaria Kunth, Echinchloa crusgalli, and Sagittaria trifolia), achieving 91.1% mAP and an inference speed of 85.7 FPS [22]. [PERSON] and [PERSON] proposed a small object detection model based on YOLOv5, \"YOLO-EP\", for detecting Pomacea canaliculata on rice [23]. However, although previous studies have been able to identify six types of weeds in rice fields, there is still a lack of relevant research on the identification of three common weeds in Hunan rice fields: Alternanthera sessilis, Polygonum lapathifolium, and Beckmannia syzigachne. Furthermore, the YOLOv5 algorithm still has limitations such as insufficient accuracy in detecting small objects, relatively low localization precision, and inadequate handling capabilities for dense scenes. The YOLOv8 model, released as an open-source project by Ultralytics on 10 January 2023, is a significant update to YOLOv5. It achieves better performance on the COCO dataset [24] while also offering lower computational complexity. Nonetheless, its application effectiveness for weed detection in complex rice field environments still requires further research. In summary, there remains a need for a high-precision weed detection model for rice fields that can handle a complex vegetation environment, the similar morphology and color of weeds, and crops and varying lighting conditions. We propose a rice field weed detection algorithm based on YOLOv8, called GE-YOLO to identify three major weeds in rice fields in Hunan, China: Alternanthera sessilis(AS), Polygonum lapathifolium(PL), and Beckmannia syzigachne(BS).