## 4 Discussion and Conclusions  
The problem of the automatic identification of anthracose on walnut tree leaves has been tackled with the use of deep learning algorithms, specifically convolutional neural networks. A total of 4.491 images was acquired, balanced in terms of healthy and infected leaves depictions. A number of pre-processing techniques, such as fast Fourier transform and background removal, were tested in order to evaluate their contribution to the increase of performance. Several CNN architectures were tested, with accuracies ranging from 92.4% to 98.7%, leading to the one that performed best under all pre-processing scenarios. The proposed methodology was designed from the ground up in order to address the specific issues of the anthracose-detection problem. Initially, we needed to address the background issue, and investigate whether or not the background plays any role in the accuracy of our classifier. Another issue that needed addressing was the type of images that we would process, meaning if they would be colored or monochromatic. Colored images contained the color information, which is important in this specific use case, since anthracose discolors areas of the green leaf into brown spots. However, there is value in the monochromatic approach, since the images can be taken in different times within a day (or night), and the color variations might change. Monochromatic images can create a more generalized classifier, even if the accuracy appears to be lower, because it diminishes this color dependency. Because of the reduced accuracy of the monochromatic approach, a feature extractor was selected and tested on its performance. The fast Fourier transform indeed improved the results of the monochromatic approach by extracting edge information with a high pass filter, leading to clearer view of the leaves' abrupt changes on their surface. The best performing algorithm was then compared with a series of state-of-the-art CNNs commonly used for image classification problems. We note that our algorithm for the particular problem performed equally, and in some cases even better, compared to these algorithms. This study validates the premise that CNNs are algorithms that can offer high accuracy in image classification-based problems. This has a direct application on precision agriculture where the automatic identification of diseases is crucial for the crops. The main outcomes of this study can be summarized as follows: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **VGG16** & **DenseNet121** & **ResNet50** & **Inception V3** & **Proposed CNN** \\ \hline _Validation accuracy_ & 96.875 & 99.049 & 98.505 & 99.290 & 99.006 \\ _Validation loss_ & 0.096 & 0.033 & 0.050 & 0.027 & 0.031 \\ _Testing accuracy_ & 95.781 & 96.577 & 98.363 & 99.375 & 98.719 \\ _Testing loss_ & 0.120 & 0.071 & 0.067 & 0.013 & 0.049 \\ _Execution time (s)_ & 1391.31 & 1866.893 & 1290.42 & 1712.213 & 989.185 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of most common CNNs with the proposed CNN. The proposed CNN method exhibits outstanding performance when RGB analysis is performed for the examined images of this case study. This can be emanated from the fact that the results produced from the application of the CNN architecture are based on distinct features that appear specifically on the anthracose-infected leaves, compared to the healthy leaves, i.e. brown spots and areas. 2. The fast Fourier transform method seems to be of major significance in feature extraction in the case of the grayscale images, because it accentuates the abrupt changes and edges of the leaves. This denotes that the infected leaves have more edgy features than the healthy ones. 3. The proposed CNN architecture exhibits high performance in all scenarios in which it has been tested considering the case of anthracose disease identification on walnut tree leaves. As it is observed from Tables 7 and 8, the accuracies range from 92.4% to 98.7%. 4.