## 4 Discussion and Conclusions
The proposed CNN architecture exhibits better, or similar, performance to well-known CNN architectures (i.e., DenseNet121, VGG16, ResNet50 and InceptionV3) which have been efficiently used as benchmarks in image processing problems for the past years. 5. Overall, for the purpose of image analysis and classification, the CNN methodology is proved to be proper for complex image classification tasks, such as the one under hand here, when a large number of images is considered, outweighing the popular CNN architectures for image analysis, such as DenseNet121, VGG16, ResNet50 and InceptionV3. Future plans include the further investigation of the proposed architecture with images collected by infrared, near-infrared and RGB cameras, as well as with images taken at all times of day and in all kinds of weather. This will help us build a more robust classifier that will be even more invariant to external conditions. Additionally, the algorithmic part of this study will be focused towards the development and application of hybrid algorithms that utilize fuzzy cognitive maps in convolutional neural networks in order to integrate experts' knowledge on this problem. The aim is to increase furthermore the accuracy of the model while simultaneously offering interpretability to the classification process. Conceptualization, A.A. and D.B. ; methodology, A.A. and E.P. ; software, A.A. and G.A. ; validation, A.A. and E.P. ; formal analysis, A.A.; investigation, G.A. ; data curation, G.A. ; writing--original draft preparation, A.A.; writing--review and editing, A.A., E.P., and D.B. ; visualization, A.A. and G.A. ; supervision, E.P. and D.B. ; project administration, D.B. All authors have read and agreed to the published version of the manuscript. The work was supported by the project \"Research Synergy to address major challenges in the nexus: energy-environment-agricultural production (Food, Water, Materials)\"--NEXUS, funded by the Greek Secretariat for Research and Technology (GSRT)--Pr. No. MIS 5002496. The authors declare no conflict of interest. * [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] Leaf Disease Detection and Classification using Neural Networks. _Int. J. Adv. Res. Comput. Commun. Eng._**2016**, \(5\), 207-210. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON] Classification of diseased plant leaves using neural network algorithms. _ARPN J. Eng. Appl. Sci._**2015**, _10_, 1913-1919. * [PERSON] et al. (2019) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. _Comput. Electron. Agric._**2019**, _161_, 280-290. [CrossRef]
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