# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
## 3 Methodology
### Classification Using the SqueezeNet Architecture  
SqueezeNet was introduced with five modules. It is claimed that the \(3\times 3\) kernel size should be reduced to \(1\times 1\), reducing the size of the overall parameter. Downsampling is also reduced into layers. More feature maps are thus learned by the layers. The introduced fire module contains the squeeze layer, and it has \(1\times 1\) filters. They are fed an expanding  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Categories** & **Activations** & **Weights** \\ \hline Convolve-4 & Grouped Convolution & \(13\times 13\times 384\) & \(3\times 3\times 192\times 192\) \\ \hline ReLU-4 & ReLU & \(13\times 13\times 384\) & - \\ \hline Convolve-5 & Grouped Convolution & \(13\times 13\times 256\) & \(3\times 3\times 192\times 128\) \\ \hline ReLU-5 & ReLU & \(13\times 13\times 256\) & - \\ \hline Pool-5 & Max-Pooling & \(6\times 6\times 256\) & - \\ \hline FC-6 & Fully Connected & \(1\times 1\times 4096\) & \(4096\times 9216\) \\ \hline ReLU-6 & ReLU & \(1\times 1\times 4096\) & - \\ \hline Drop-6 & Dropout & \(1\times 1\times 4096\) & - \\ \hline Fc-7 & Fully Connected & \(1\times 14096\) & \(4096\times 4096\) \\ \hline ReLU-7 & ReLU & \(1\times 1\times 4096\) & - \\ \hline Drop-7 & Dropout & \(1\times 1\times 4096\) & - \\ \hline FC-8 & Fully Connected & \(1\times 1\times 5\) & \(5\times 4096\) \\ \hline Softmax & Softmax & \(1\times 1\times 5\) & - \\ \hline Class Output & Classification & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Categories** & **Activations** & **Weights** \\ \hline Inception-3a-\(1\times 1\) & Convolution & \(28\times 28\times 64\) & \(1\times 1\times 192\times 64\) \\ \hline Inception-3a-\(3\times 3\) & Convolution & \(28\times 28\times 128\) & \(3\times 3\times 196\times 128\) \\ \hline Inception-3a-\(5\times 5\) & Convolution & \(28\times 28\times 32\) & \(5\times 5\times 16\times 132\) \\ \hline Inception-4a-\(1\times 1\) & Convolution & \(14\times 14\times 192\) & \(1\times 1\times 480\times 192\) \\ \hline Inception-4a-\(3\times 3\) & Convolution & \(14\times 14\times 208\) & \(3\times 3\times 96\times 208\) \\ \hline Inception-4a-\(5\times 5\) & Convolution & \(14\times 14\times 48\) & \(5\times 5\times 16\times 48\) \\ \hline Inception-5a-\(1\times 1\) & Convolution & \(7\times 7\times 256\) & \(1\times 1\times 832\times 256\) \\ \hline Inception-5a-\(3\times 3\) & Convolution & \(7\times 7\times 320\) & \(3\times 3\times 160\times 320\) \\ \hline Inception-5a-\(5\times 5\) & Convolution & \(7\times 7\times 128\) & \(5\times 5\times 32\times 128\) \\ \hline FC & Fully Connected & \(1\times 1\times 5\) & \(5\times 1024\) \\ \hline Softmax & Softmax & \(1\times 1\times 5\) & - \\ \hline Classification & Classification Output & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: GoogLeNet network used for guava disease detection.  
layer that is a mixture of \(1\times 1\) and \(3\times 3\) kernels. The SqueezeNet layer architecture and parameters with altered layers are shown in Table 4.  
The fire modules in the hyperparameter continuation produce three tunable parameters, namely s1 \(\times\) 1, e1 \(\times\) 1, and e3 \(\times\) 3. AlexNet's level of accuracy was achieved by the actual SqueezeNet with 50\(\times\) fewer parameters, and the model size was reduced to just 0.5 MB because of the decrease in the kernel sizes and the fire modules used in this architecture.