# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 5 Discussion and Conclusions
Additionally, when shallow CNN was used together with regularization, we obtained a slight improvement in performance (96% accuracy). Our method of extracting the ROI specifies an adaptive threshold for each CUT based on the average intensity values of the reference cells in the 2D sliding window. Problems caused by busy backgrounds and specular lighting are well-known in the literature, but in this study, we report no impact of such factors on the performance of our algorithm. This work analysed the performance of several CNN architectures: Inception V3, DenseNet 121, DenseNet 201, MobileNet, VGG16, EfficientNetB0, LeNet, ResNet50 and our proposed shallow CNN. In the future, the current findings of this work can be extended to: (i) Disease classification problems in other crops where yield is largely affected by crop disease, thus affecting the livelihoods of farmers, such as beans, bananas and cassava. (ii) Develop new algorithms for simultaneous multi-label leaf disease classification and quantification. Developing a robust method for quantifying leaf diseases with multiple symptoms is the next logical step. (iii) Validate the proposed CNN model on standard image processing datasets such as ImageNet and CiFar. Conceptualization, H.D.M. and G.O. ; Formal analysis, C.N. ; Methodology, F.M. ; Supervision, D.O. and A.N. ; Writing--review & editing, G.O. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The augmented PlantVillage dataset is available from the authors. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} CAM & Class Activation Map \\ CNN & Convolutional Neural Network \\ CUT & Cell Under Test \\ DL & Deep Learning \\ ML & Machine Learning \\ ROI & Region of Interest \\ SVM & Support Vector Machine \\ \end{tabular}  References  
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