# A Deep Residual U-Type Network for Semantic Segmentation of Orchard Environments
## 1 Introduction
The front-end module is an improved structure of VGG-16 (Visual Geometry Group Network) fusion cavity convolution, and the context module is a cascade of the convolutional layer with different expansion coefficients. The two-stage training method was adopted to train the model, and the model showed good adaptability to shadow interference. [PERSON] used the YOLOV3 (You Only Look Once) convolutional neural network to extract the feature points on road images from orchards, generated the navigation line by the least square method [13], and conducted experiments in a variety of different natural environments, with a deviation of 3.5 cm. [PERSON] [14] combined the strengths of U-Net and residual learning to construct a seven-layer deep residual U-type network for road extraction from aerial images and achieved good results. [PERSON] [15] used a deep residual U-Net convolutional nerve network for lung CT image segmentation in the field of biomedicine, and the segmentation effect was significantly improved, which effectively helped in the early diagnosis and treatment of lung diseases. Environment recognition algorithms based on deep learning have greatly improved the accuracy of recognition [16], but they are mainly used in the recognition of structured roads or environments with few distractors, and the application scenarios are relatively singular. The orchard environment is complex and changeable, and the span between different objects is larger. Recognizing these objects requires a deeper network, but this will cause problems such as gradient disappearance, increased parameters, and difficulty in training. Therefore, this paper takes a U-type network as the main body of the network model; gives full play to its advantages, such as fewer parameters and high recognition accuracy; and adds residual blocks in the feature extraction process to deepen the network and improve the extraction ability of object edge information, finally building an orchard environment recognition algorithm based on a deep residual U-shaped network. Pixel-level semantic segmentation is used for the image of the orchard environment to obtain various information on the orchard environment, laying the foundation for autonomous operation of the gardening tractor in the future. The main contributions of this paper are as follows: Section 1 introduces the current research status of environment identification and proposes an orchard environment identification algorithm based on the deep residual U-type network. Section 2 describes the acquisition and processing of orchard environment data sets. Section 3 presents the construction of the orchard environment model and constructs the deep residual U-type network segmentation mode by analyzing the characteristics of the residual network and the U-type network and combining the actual needs of the orchard environment identification. In the fourth section, experimental comparative analysis is carried out on the fully convolutional neural network, the U-type network, the Front-end+Large network, and the deep residual U-type network. Finally, the conclusions and future work are presented in Section 5.