# GE-YOLO for Weed Detection in Rice Paddy Fields
## 2 Materials and Methods  
### Dataset Building  
The rice field weed data were captured between April and May 2024 in two different rice fields (Zoomlion Smart Agriculture Base in Yuelu, Changsha and Yingchang Agricultural Base in Liuyang, Changsha, as shown in Figure 1).  
We conducted a total of three data collection sessions. The first session took place at Zoomlion Smart Agriculture Base in Yuelu on 22 April 2024, under cloudy weather conditions, with the rice plants in the seedling stage. The second session was carried out at Yingchang Agriculture Base in Liuyang on 27 April 2024, under overcast conditions that later turned to light rain, with the rice plants in the tillering stage. The third session was conducted again at Zoomlion Smart Agriculture Base in Yuelu on 17 May 2024, under clear skies with intense sunlight, with the rice plants in the jointing stage. The images captured during these three sessions across the two locations encompass different growth stages of rice, as well as various species and distributions of weeds, along with diverse lighting conditions under different weather scenarios. Furthermore, the randomness of data distribution, varying degrees of occlusion, weed density, and different scales of photography were also considered. These measures could enhance the representativeness and generalizability of the dataset.  
The original images had a resolution of 3024 \(\times\) 4032 pixels. A total of 995 images were captured: 126 from Yingchang Agriculture Base in Liuyang and 869 from Zoomlion Smart Agriculture Base in Yuelu. These images include three common types of weeds found in the Hunan region: Beckmannia syzigachne, Alternanthera sessilis, and Polygonum lapathifolium. Some sample images from the dataset are shown in Figure 2. The data were further screened, classified, and annotated with the guidance of agricultural experts.  
The data annotation process was conducted using the online annotation platform Roboflow. During the annotation process, the fundamental principle was to ensure that the bounding boxes precisely and completely enclosed the characteristic parts of the target weeds (such as leaves, spikes, etc.). In cases where multiple weeds were present in an image (as shown in Figure 3a), each weed was annotated separately. For scenarios with densely distributed weeds (as shown in Figure 3b,c), each individual weed was distinguished  
Figure 1: Data Collection Environment, (**a**) Zoomlion Smart Agriculture Base in Yuelu, Changsha, (**b**) Yingchang Agriculture Base in Liuyang, Changsha.  
Figure 2: Some images from the dataset, where (**a**) are Alternanthera sessilis(AS), (**b**) are Polygonum lapathifolium(PL), (**c**) are Beckmannia syzigachne(BS), (**d**) are images without targets.  
and annotated separately. In cases where weeds were partially occluded (as shown in Figure 3d), the annotation was based on the visible parts of the weeds.  
To build a robust neural network, a large amount of training data are required to improve the network's detection performance and generalization ability. Additionally, insufficient training data can make it difficult for the network to converge during training, leading to poor results. Therefore, we employed five data augmentation techniques based on the original images: flip (horizontal, vertical), rotation (between \(-11^{\circ}\) and \(+11^{\circ}\)), brightness (between \(-20\%\) and \(+20\%\)), blur (up to 0.7 px), and noise. By combining these augmentation techniques, we expanded the dataset to a total of 3297 images. Table 1 shows the distribution of the three types of rice field weeds and the dataset partitioning.  
In the actual training process, we use AS, PL, and BS, to refer to Alternanthera sessilis, Polygonum lapathifolium, and Beckmannia syzigachne, respectively.