# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
## 3 Methodology  
The automation of plant disease monitoring is taking the place of manual monitoring. Many researchers have used real-time experimentation of plant disease monitoring and achieved satisfying results. A local Pakistani dataset was collected for guava plant and fruit disease detection in the proposed study. Data augmentation was used to meet the big data usage challenge, where it was also used to cover model overfitting problems. Data augmentation was performed using the affine transformation method; to enhance the region of interest (ROI), unsharp masking and the histogram equalization method were performed, better sharpening the ROI and removing any existing noise in the augmented data. The final augmented and enhanced data were fed into various fine-tuned state-of-the-art classification methods. All the steps are shown in Figure 1.  
In the framework, augmented and enhanced images were given to 5 different predefined architectures by replacing their last layer according to the given data classes. AlexNet was the first model in the ImageNet competition that changed the image classification and object detection using deep-learning models. SqueezeNet, GoogLeNet, and ResNet followed with many others. Famous ones with different kinds of architectures were used to check the effectiveness of a given local guava dataset. The proposed method showed an initial step on a newly collected local Pakistani dataset, where more methods can be adopted using a different ML and DL technology. The details of the dataset before and after augmentation and the details of other used CNN architectures are shown in Section 3.1 by discussing their fine-tuned parameters, with the weights in Sections 3.2-3.4. The achieved results on the validation data are shown in Section 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Year** & **Species** & **Domain** & **Method** & **Results** \\ \hline
[38] & 2021 & Plant-Village & Deep learning & \
\begin{tabular}{c} C-GAN and \\ DenseNet121-based \\ transfer learning \\ \end{tabular} &
\begin{tabular}{c} Accuracy (5 classes) = 99.51\% \\ Accuracy (7 classes) = 98.65\% \\ Accuracy (10 classes) = 97.11\% \\ \end{tabular} \\ \hline
[4] & 2021 & Guava plant disease & Machine learning &
\begin{tabular}{c} HSV, RGB, LBP, and \\ classical machine \\ learning methods \\ \end{tabular} & 99\% accuracy \\ \hline
[39] & 2020 & Cotton plant disease & Deep learning & Proposed CNN & \(\sim\) \\ \hline
[40] & 2019 & Plant disease dataset & Deep learning &
\begin{tabular}{c} Traditional augmentation \\ and GAN for data \\ generation and \\ neural network \\ \end{tabular} & Accuracy = 93.67\% \\ \hline
[41] & 2019 & Tomato and brijal & Deep learning & \
\begin{tabular}{c} GLCM, adaptive \\ neuro-fuzzy system \\ \end{tabular} &
\begin{tabular}{c} Tomato accuracy = 90.7\%, \\ brijal accuracy = 98.0\% \\ \end{tabular} \\ \hline
[42] & 2019 & Potato tuber & Deep learning & CNN & 90–10 split training–testing \\  & & & & accuracy = 96\% \\ \hline
[33] & 2018 & Open plant dataset & Deep learning &
\begin{tabular}{c} AlexNet, VGG, and other \\ CNN \\ \end{tabular} & Best accuracy = 99.53\% \\ \hline
[43] & 2018 & Papaya leaves & Machine learning &
\begin{tabular}{c} HOG features, random \\ forest classifier \\ \end{tabular} & Accuracy = 70.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of recent studies on the detection of guava diseases.