# Lightweight Semantic Segmentation Network for Real-Time Weed Mapping Using Unmanned Aerial Vehicles  
[PERSON]  
1 College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China; 2 [EMAIL_ADDRESS] (J.D. ); [EMAIL_ADDRESS] (Z.Z.) [PERSON]  
1 College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China; 2 [EMAIL_ADDRESS] (J.D. ); [EMAIL_ADDRESS] (Z.Z.) [PERSON]  
3 College of Computer Sciences, Guangdong Polytechnic Normal University, Zhongshan Road, Guangzhou 510642, China; [EMAIL_ADDRESS] (Z.Z.) [PERSON]  
prescription maps. Methods for obtaining orthophoto maps include satellite imaging and UAV imaging [2]. The resulting remote sensing images are analyzed to map weed distribution information, which can provide decision-making support information for precise spraying applications [3]. Based on their high spatial resolution, UAVs have been found to be appropriate for weed mapping tasks. [PERSON] et al. [4] collected UAV images and achieved good performance for weed mapping using a semi-supervised method. [PERSON] et al. [5] used UAV multispectral images to classify maize and weeds. However, in previous studies, most image processing has been conducted offline, which results in a significant time gap between image collection and spraying applications. This disadvantage significantly limits the applications of SSWM because agronomy conditions may change during large time gaps. In SSWM applications, the time gaps should be less than one day so that the herbicide treatment can be applied before the agronomy conditions change. One possible solution to address this issue is to utilize real-time image processing onboard UAVs. Based on real-time data collection performed by embedded devices, UAVs can perform corresponding spraying actions immediately, which can solve the problem of time gaps in the current model of SSWM. Although there has been no related work in the domain of SSWM research, we identified some related studies in industrial domains. One of the barriers in real-time image processing is that the image data is large and difficult to process by embedded devices, and some studies have exploited to reduce the amount of data while maintaining the monitoring objective. [PERSON] et al. [6] used a 360-degree camera mounted on a UAV for fire detection. The use of 360-degree camera significantly reduces the amount of data, which is meaningful in real-time processing scenarios. In addition to the data catastrophe, the employment of deep learning models utilized by connecting desktops to embedded devices is a major challenge in the industrialization of artificial intelligence. Currently, most studies on employing deep learning models using embedded devices focus on reducing the model size while maintaining reasonable accuracy. [PERSON] et al. [7] introduced a tiny solid-state drive architecture that requires a storage space of only 2.3 MB to achieve a mean average precision of 61.3% on the VOC 2007 challenge. [PERSON] et al. [8] proposed a lightweight model for multiple-object detection and tracking that can integrate a deep-learning-based association metric approach with simple online and real-time tracking. Their algorithm was deployed on an NVIDIA Jetson TX module and Intel Neural Compute Stick. The results demonstrated the effectiveness of their algorithms for real-time experiments onboard UAVs. [PERSON] et al. [9] proposed a modified MobileNetV2 architecture for fire classification. They modified the last fully-connected layer of the MobileNetV2 architecture to fit the fire detection task, and the resulting model exhibited better performance than that of previous models. The proposed model was deployed on a Raspberry Pi 3B device and achieved a speed of 5 fps with an accuracy of 0.92 on the dataset presented in [10]. However, regarding the requirements of SSWM applications, there are some limitations to the methods proposed in the aforementioned studies.