# A Deep Residual U-Type Network for Semantic Segmentation of Orchard Environments
## 1 Introduction  
The \"Made in China 2025 strategy\" puts forward new requirements for China's agricultural equipment and requires continuous improvement of the ability of agricultural machinery intelligence and precision operation [1]. The horticultural tractor is an important piece of agricultural machinery working in orchards, forest gardens, and other environments. One of the basic tasks of realizing intelligent operation of the horticultural tractor is to identify the working environment. With the development of science and technology, more and more methods are being used for environmental recognition, such as lidar for scanning and recognizing the surrounding environment. However, due to the high cost of lidar, it is difficult to apply to agricultural products. In contrast, the use of ordinary cameras as sensors has many advantages, like comprehensive information collection and low price [2]. For vision-based environmental recognition, related algorithms based on the target of achieving rapid and accurate recognition have mainly been formulated. In the research of environment recognition, [PERSON] [3] developed a small autonomous navigation vehicle in a peach orchard based on machine vision. The machine vision system was based on the use of a multispectral camera to capture real-time images and process the images to obtain trajectories for autonomous navigation. [PERSON] [4] used naive Bayesian classification to detect the boundary between the trunks and the ground of an orchard and proposed an algorithm to determine the centerline of the orchard roadfor automatic driving of the orchard's autonomous navigation vehicle. In order to enable agricultural robots to extract effective navigation information in a complex, open, non-structural farmland environment, and solve the instability of navigation information extraction algorithm caused by light changes, [PERSON] [5] proposed to use the color constancy theory to solve lighting problems in machine vision navigation. [PERSON] [6] proposed a vision-based agricultural vehicle guidance system, using the Hough transform method to extract guidance parameters, and designed a path recognition method based on centerline detection and erosion algorithm. The environmental recognition method in the literature basically solves the problem of recognition in a specific environment and has specificity, which has certain limitations. In recent years, there has been rapid development of deep learning, which has shown excellent performance in environment recognition [7] and target detection [8]. Environment recognition algorithms based on deep learning have the advantages of strong robustness and high accuracy. In the research of environment recognition based on deep learning, [PERSON] [9] used the high-order features in convolutional neural network learning scenarios to segment road scenes and generated training labels by training on image data sets, then used the new textures based on color layer fusion to obtain the maximum consistency of the road area, and finally combined the offline and online information to detect urban road areas. [PERSON] [10] proposed the Segnet network for road scene understanding. The network architecture is based on an encoding-decoding network, but the segmentation of road boundary details still needs to be improved. He [11] used the Spatial Pyramid Pooling Network (SPP-Net) to extract roads from remote sensing images and used structural similarity (SSIM) as the loss function for road extraction, which can extract the fuzzy prediction in the extraction results and improve the image quality of the extracted roads. [PERSON] [12] constructed a semantic segmentation model of field road scenes based on the hollow convolutional neural network for complex field road scenes in hilly and mountainous areas. The model includes a front-end module and a context module.