# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
## 3 Methodology
### Classification Using the ResNet-50 Architecture  
ResNet was introduced with the residual block concept mainly to answer the overfitting issue created in DL models. It uses a considerable number of layers, such as 50, 101, and 152. As ti is suggested by GoogLeNet to use a small kernel size, it uses small convolutional kernels where denser or more layers are used to meet or improve the validity of the data. The introduced residual block uses a \(1\times 1\) layer that reduces the dimension, a \(3\times 3\) layer, and a \(1\times 1\) layer used to restore the dimensions of the given input. The layer-based ResNet was used, so we used a 50- and 101-layer architecture; the 50-layer architecture of ResNet is shown in Table 5.  
The basic residual branches of ResNet-50 with their learned parameters in guava disease detection are shown in Table 5. The residual block re-concatenates spatial information from the previous block to preserve information in each calculated feature map of the residual block. For the proposed framework, the last layers are altered with five categories to classify them on the basis of previous learning on the augmented guava data of the proposed study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Categories** & **Activations** & **Weights** \\ \hline Fire3-Squeeze-1\(\times\)1 & Convolution & \(56\times 56\times 16\) & \(1\times 1\times 128\times 16\) \\ \hline Fire4-Squeeze-1\(\times\)1 & Convolution & \(28\times 28\times 32\) & \(1\times 1\times 128\times 32\) \\ \hline Fire5-Squeeze-1\(\times\)1 & Convolution & \(28\times 28\times 32\) & \(1\times 1\times 256\times 32\) \\ \hline Fire6-Squeeze-1\(\times\)1 & Convolution & \(14\times 14\times 48\) & \(1\times 1\times 256\times 48\) \\ \hline Fire7-Squeeze-1\(\times\)1 & Convolution & \(14\times 14\times 48\) & \(1\times 1\times 384\times 48\) \\ \hline Fire8-Squeeze-1\(\times\)1 & Convolution & \(14\times 14\times 64\) & \(1\times 1\times 384\times 64\) \\ \hline Fire9-Squeeze-1\(\times\)1 & Convolution & \(14\times 14\times 64\) & \(1\times 1\times 512\times 64\) \\ \hline Last-convolve & Convolution & \(14\times 14\times 5\) & \(1\times 1\times 512\times 5\) \\ \hline ReLU-Convolve & ReLU & \(14\times 14\times 5\) & \(1\times 1\times 512\times 5\) \\ \hline Pool-4 & Global Average Pooling & \(1\times 1\times 5\) & - \\ \hline Softmax & Softmax & \(1\times 1\times 5\) & - \\ \hline Classification & Classification Output & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: SqueezeNet network used for guava disease detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Categories** & **Activations** & **Weights** \\ \hline Res-2a & Convolution & \(56\times 56\times 256\) & \(1\times 1\times 64\times 256\) \\ \hline Res-3a & Convolution & \(28\times 28\times 512\) & \(1\times 1\times 256\times 512\) \\ \hline Res-4a & Convolution & \(14\times 14\times 1024\) & \(1\times 1\times 512\times 1024\) \\ \hline Res-5a & Convolution & \(7\times 7\times 2048\) & \(1\times 1\times 1024\times 2048\) \\ \hline FC & Fully Connected & \(1\times 1\times 5\) & \(5\times 2048\) \\ \hline Softmax & Softmax & \(1\times 1\times 5\) & - \\ \hline Class-Output & Classification & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: ResNet-50 network used for guava disease detection.