# A Deep Residual U-Type Network for Semantic Segmentation of Orchard Environments
## 4 Orchard Environment Identification Test
### Test Results and Analysis  
When the deep residual U-type network model was trained, the model was saved every five iterations, and the model with the highest average intersection ratio among all models was selected as the test model. In order to verify the superiority of the proposednetwork model, it was compared with the fully convolutional neural network model, the U-type network model, and the Front-end+Large network. Figures 5-8 show the loss values and _MloU_ of each iteration of the four network models. Table 2 reports the highest Pixel Accuracy (_PA_) and the highest _MloU_ for category segmentation. From its training effects, the fully convolutional neural network has a larger floating amplitude and higher frequency. After about 100 iterations, the loss value and the _MloU_ tended to stabilize, but the floating range was large. The fully convolutional neural network discards the fully connected layer and uses deconvolution to realize semantic segmentation. When recognizing the complex environment of the orchard, there are more network parameters in the fully convolutional neural network, which leads to problems such as the segmentation of the details in the image not being clear enough, the segmentation boundary between different categories being blurred, and the training time being long. The training effect of the U-type network in Figure 6 was significantly better than that  
Figure 5: The iteration loss and mean intersection over union (_MloU_) of the fully convolution neural network. Figure 6: The iteration loss and _MloU_ of the U-type network. Figure 7: The iteration loss and _MloU_ of the Front-end+Large network. of the fully convolutional neural network. The network tended to be stable after about 60 iterations. Up-sampling was adopted to map the feature images, which greatly reduced the network parameters. However, there were still fluctuations in the preliminary training process, which was due to insufficient network depth and poor ability to distinguish the boundaries of different categories in the image during training. The effect of Front-end-Large network training in Figure 7 was significantly improved compared with that for the first two networks. The fluctuation in the previous training was small and tended to be stable around 54 iterations. The training effect of the deep residual U-type network in Figure 8 was significantly better than that of the first three networks. There was no obvious fluctuation in the early training period, the change in the loss value and _MloU_ was small after about 40 iterations, and the overall training time was short and stable. The deep residual U-type network adopts the U-type network structure but adds a residual block in the coding layer and fuses the image feature information, which better processes the image boundary information. In addition, a jump connection and constraints were added to the decoding layer, which effectively reduced network parameters. Among the four networks, the deep residual U-type network had the lowest loss value, the highest _MloU_, and the best training effect. As can be seen from Table 2, in the category segmentation of the orchard environment, the four semantic segmentation network models had high accuracy in pixel recognition for backgrounds and roads, but low accuracy in recognition of fruit trees and debris. The average intersection ratio of the deep residual U-type network proposed in this paper was 85.95%, which is higher than those of the first three network models, and achieved better results in orchard environment recognition. Figure 9 shows the respective semantic segmentation prediction images from the four network models. Among them, the segmentation image generated by the fully convolutional neural network model has the following shortcomings: One is that some areas' categories were lost in the segmentation, and small objects such as small branches could not be identified.