# GE-YOLO for Weed Detection in Rice Paddy Fields
## 3 Results and Discussions
### Discussion  
To date, while research on weed detection in rice fields has made some progress, challenges such as complex vegetation environments, water surface reflections, and weeds with similar shapes and colors to crops remain unresolved. The generalization and robustness of existing algorithms need further enhancement. In response, we have proposed GE-YOLO, a network specifically designed for detecting weeds in rice paddies, capable of handling various weed targets under different sizes, lighting conditions, and occlusions.  
Our network incorporates several innovative mechanisms to enhance robustness and generalization: (a) GE-YOLO introduces a feature fusion distribution network that directly performs multi-scale feature fusion, effectively enhancing the network's ability to represent and detect weed targets of different scales in complex environments. This enables the network to maintain high precision and consistency in detection performance across various scales, types of weeds, and shooting angles. (b) GE-YOLO incorporates an EMA mechanism that strengthens the network's focus on target regions within the feature maps. This allows the network to accurately identify weed regions even in complex environments where weeds and rice exhibit similar colors and significant overlap. (c) GE-YOLO adopts the GIoU loss function, which provides smoother gradient information and lower computational complexity. This optimization directs the network's performance toward effectively identifying weeds in paddy fields under complex conditions, making it better suited for the task of weed detection in rice fields.  
Moreover, through our field efforts, we have constructed a high-quality dataset of rice paddy weeds that encompasses diverse growing environments, various growth stages of rice, different lighting conditions under varying weather, as well as varying  
Figure 12: Test results under occluded occasions, where (**a**) are AS, (**b**) are PL, (**c**) are BS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Weed Species** & **Precision/\%** & **Recall/\%** & **mAP/\%** \\ \hline ALL Species & 90 & 81.6 & 88.7 \\ AS & 88 & 72.3 & 82.1 \\ PL & 91.3 & 92.5 & 95.3 \\ BS & 90.7 & 80 & 88.7 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Weed detection results under occlusion conditions.  
-weed densities and distributions. Our network effectively learns the features of the three common weed species in Hunan rice fields: Alternanthera sessilis, Polygonum lapathifolium, and Beckmannia syzigachne, which are visually similar to rice crops. The experimental results also validate that our model exhibits excellent robustness and generalization capabilities.  
However, there are some limitations to our research. Due to practical constraints, we have only collected data for three common weed species. In reality, there are additional weed species in rice fields that vary by regional environment. The common weed species need detection in rice fields also include Echinocha crus-galli, Monochoria vaginalis, Cyperus rotundus, Cyperus serotinus, Commelina communis, Potamogeton distinctus, and Lemaa minor. Therefore, we plan to expand our dataset in future research. Additionally, there is still room for improvement in our model, and we will continue to explore ways to enhance it to better suit various rice field weed detection tasks.