# Clover Dry Matter Predictor Based on Semantic Segmentation Network and Random Forest
## 2 Methods
### DeepLabv3+
#### 2.2.2 ASPP Based on Squeeze and Extraction Networks  
The conventional convolution employs a fixed-size kernel, calculating each image position with a consistent stride. The receptive field size is contingent upon the kernel dimensions, expanding in line with network layer augmentation. However, this expansion escalates computational demands. In the context of ASPP, dilated convolution introduces the notion of an expansion rate into conventional convolution, governing the kernel's sampling interval through the expansion rate, as illustrated in Figure 4. This innovation enhances the kernel's voids, thus amplifying the receptive field, and consequently extracting more extensive features. In essence, dilated convolution in ASPP employs the concept of expansion rate within conventional convolution, manipulating the sampling stride of the convolutional kernel to augment voids within the kernel. This approach expands the receptive field, yielding features endowed with more expansive receptive fields.  
Designating the initial feature map as \(X\) and the resultant feature map from the cavity convolution as \(Y\), the operation of Dilated convolution can be formalized as follows:  
\[Y[i,j]=\sum\
olimits_{m}\sum\
olimits_{n}X[i+m\cdot d,j+n\cdot d]\cdot K[m,n] \tag{1}\]  
Here, \(i\) and \(j\) represent the spatial coordinates of the resulting feature map, while \(m\) and \(n\) indicate the coordinates of the convolution kernel. The symbol \(k\) pertains to the weight assigned to the convolution kernel at the corresponding spatial position, and \(d\) signifies the expansion rate. In contrast, the conventional convolution can be conceived as a specific instance of dilated convolution, characterized by an expansion rate of 1.  
Figure 2 shows the model structure of ASPP in DeepLabv3+, which uses multiple parallel convolutional branches, each using a different size of dilated convolutional kernel. In this way, the model can capture features under different sensory fields, which enables the model to better understand the objects in the image as well as their contextual information.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input** & **Operator** & **t** & **c** & **n** & **s** \\ \hline \(1024^{2}\times 3\) & conv2d & - & 32 & 1 & 2 \\ \(512^{2}\times 32\) & bottleneck & 1 & 16 & 1 & 1 \\ \(512^{2}\times 16\) & bottleneck & 6 & 24 & 2 & 2 \\ \(256^{2}\times 24\) & bottleneck & 6 & 32 & 3 & 2 \\ \(128^{2}\times 32\) & bottleneck & 6 & 64 & 4 & 2 \\ \(64^{2}\times 64\) & bottleneck & 6 & 96 & 3 & 1 \\ \(64^{2}\times 96\) & bottleneck & 6 & 160 & 3 & 2 \\ \(32^{2}\times 160\) & bottleneck & 6 & 320 & 1 & 1 \\ \(32^{2}\times 320\) & conv2d \(1\times 1\) & - & 1280 & 1 & 1 \\ \(32^{2}\times 1280\) & avgpool \(32\times 32\) & - & - & 1 & - \\ \(1\times 1\times 1280\) & conv2d \(1\times 1\) & - & k & - & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mobilenetv2 network architecture.  
Figure 4: Dilated convolution schematic with 3 \(\times\) 3 convolution kernel (**a**) Expansion rate of 1, ordinary convolution (**b**) Dilated convolution with expansion rate of 2.  
In order to better fuse features at different scales, this paper introduces the SE [30] attention mechanism before the fusion of features at each scale, which can amplify the response to important features and suppress the response to unimportant features through the learning of channel weights, and is able to adaptively learn the correlation between different channels in the input feature map, and then re-calibrate the features of each channel, so as to improve the ASPP in the performance in feature extraction capability.